KICT 한국건설기술연구원

검색

ALL Subject Content Search

TOTAL 9 Current page 1/1

• AI Agents for Construction Sites: AI Service Implementation Cases and Future Directions Researcher: Won Ji-sun, Senior Researcher, Department of Future & Smart Construction Research, KICT Prologue What are the technology trends to watch in the AI market in 2025? Global companies including NVIDIA and Microsoft are unanimously pointing to "AI Agents." An AI agent is an intelligent system that perceives its surrounding environment and autonomously makes decisions to take action (Heo Jung-joon, 2024). In simple terms, it is an AI partner that independently makes judgments and achieves given goals without human intervention. It is expected that the era of AI Agents in the construction industry will soon arrive. In the future, we will manage construction sites in collaboration with "AI supervisor agents." What kind of abilities will these AI supervisor agents have? AI Agent Advancement Stages Just as autonomous driving technology can be divided into multiple stages that led to fully autonomous driving, AI agent technology is also categorized into several stages toward artificial general intelligence (AGI). Academic and industry sectors define the development stages of AI agents from various perspectives. According to one AI platform company, AGI will be implemented not as a single large model but as a collaborative form of hundreds of AI agents. The development process of AI agents can be divided into the following four stages (Jaeman An, 2024). Stage 1 is an AI service stage utilizing AI models that handle specific tasks. Stage 2 is an AI pipeline stage, in which AI agents combine multiple AI models to automate workflows. Stage 3 is the specialized AI system stage, which combines multiple AI pipelines within specific domains to solve complex problems. Stage 4 is the general-purpose AI system stage, in which hundreds of specialized AI systems capable of working across multiple domains are integrated. Each stage is expected to develop toward solving increasingly complex problems by utilizing and building upon the technologies of previous stages. Current AI technology is focused on building AI services using specific task models and early pipeline stages that combine them. This article introduces three task models specialized in analyzing civil complaints at road construction sites and an AI service prototype utilizing them, while presenting advancement strategies for progressing to the next stage. AI Agent Stage 1 Implementation Case - Road Construction Site Official Document-Based AI Civil Complaint Analysis Service The Korea Institute of Civil Engineering and Building Technology (KICT) is discovering AI services to prepare for the AI transformation era and secure data-driven construction project management technology. A survey of 50 construction workers regarding AI needs at construction sites revealed a high demand for the introduction of AI in risk management tasks that are difficult to predict and where experience is lacking, with particularly high demand for responses to civil complaints regarding construction sites. Accordingly, we introduce an AI service prototype developed to solve information utilization problems occurring in the civil complaint response process at construction sites. Official documents in the Ministry of Land, Infrastructure and Transport (MOLIT) Construction Project Management System reveal that there are more than 5,000 cases of civil complaints regarding general national road construction sites annually. To develop field demand-based AI services, interviews with supervision teams (7 people) and surveys (30 people) were conducted to identify problems and requirements in civil complaint work performance, to identify the functions necessary for problem solving. Through a priority evaluation of the derived functions, functions with high work importance that could be quickly implemented using available civil complaint data were selected (Shin Jae-young, Won Ji-seon, 2024). Among all functions, the functions that could improve the efficiency of civil complaint data analysis through AI introduction were selected. An analysis of civil complaint data characteristics determined that applying AI models to the tasks of automatically classifying 20 types of road civil complaints from vast official document data and rapidly extracting key items including complaint causes, requirements, and related facilities would be effective. Accordingly, a "Civil Complaint Type Classification Model," "Civil Complaint Cause/Requirement Recognition Model," and "Facility Recognition Model" were developed for civil complaint analysis automation. To build AI models handling three tasks, supervision team official documents and attachments accumulated over seven years were collected, and a total of 37,926 raw datasets were secured through preprocessing, including text parsing and stop word removal. Optimized models were developed by establishing task-specific labeling standards and data augmentation standards to build training datasets, and then fine-tuning the pre-trained model "KoELECTRA-BASE-v3" using transfer learning methods. The three task models specialized in civil complaint analysis were applied to functions using real-time inference methods and inference database utilization methods. Supervision teams can utilize AI services based on three civil complaint work situations (status assessment, prevention, complaint resolution) as follows: Situation 1. Status Assessment (1) AI Civil Complaint Statistics Service: When it is necessary to understand civil complaint characteristics and status at general national road sites, complaint trends and statistics are identified through the "Statistics Dashboard," and results analyzing related complaint types, frequency, causes, and requirements for keywords (e.g., status report, illegal, compensation, etc.) are confirmed through "Keyword Analysis." Situation 2. Prevention (2) Priority Management Issue Analysis Service: To prevent civil complaints and respond proactively, supervision teams review complaint statistics, trends, and keyword analysis results for the three priority management facilities (water supply/drainage facilities, power/communication facilities, buildings/livestock facilities), and check analysis reports on the issues supervision teams want to identify in advance: third-party damage, construction suspension, and construction cost changes. Situation 3. Complaint Resolution (3) Document Analysis: When uploading newly received civil complaint official documents or inputting content, AI models output inference results for the complaint type (e.g., inundation, outflow of soil), cause (e.g., localized torrential rainfall), requirements (e.g., soil support for typhoon damage recovery), and related facilities (e.g., pier, soil), enabling a rapid understanding of the complaint’s core content. (4) AI Similar Complaint Search: To find cases similar to the identified complaint content, enter search terms such as complaint type, facility name, or official document title in the search box to display 10 complaint cases in order of similarity. Users can review analyzed complaint types, causes, and requirements for each case, and download the necessary original texts for use in presenting complaint resolution measures and writing complaint review reports. AI Agent Stage 2 Advancement Strategy - LangChain Framework Introduction and RAG Utilization The AI civil complaint analysis service introduced above operates through users calling AI models via functions or utilizing databases storing inference results. For the current service to advance to the AI pipeline stage, the introduction of an AI workflow automation framework that combines multiple AI models and data processing tasks to automate work flows is necessary. Representative AI workflow automation frameworks include "LangChain," "AWS SageMaker" and "Apache Airflow," which automate tasks such as model training, data processing, and deployment, connect data flows between multiple models, and provide workflow management and scalability. Of these, LangChain, a portmanteau of the words "Language" and "Chain," is an open-source framework that helps easily develop Large Language Model (LLM)-based services (Park Tae-woong, 2024) that has established itself as the standard for AI service development using LLMs. LangChain supports the flexible integration of various LLMs, and has strengths in building natural language processing-based AI pipelines through Retrieval-Augmented Generation (RAG) functionality that provides real-time information search and document-based responses. RAG is a technology that moves away from existing LLM approaches that rely only on pre-trained data, instead connecting with external databases or search systems to reflect the latest information in real time. RAG can connect to various sources including internal databases, cloud, and the web, and is evaluated as a technology that significantly improves AI agent response quality and expands utilization possibilities. Using RAG models enables the generation of more accurate and rich answers based on real-time information, allowing the implementation of various LLM-based AI agents including intelligent document search, summarization, conversational chatbots, and data analysis report generation. By introducing LangChain and RAG to combine LLM and RAG models with fine-tuned civil complaint analysis specialized AI models, the advancement to a "Civil Complaint Response AI Agent" that analyzes complaints received in real time and automatically writes complaint review reports based on past complaint case search results becomes possible. Epilogue In the future, AI agents will acquire more capabilities through multimodal data learning, reinforcement learning-based autonomous learning, and metaverse integration. As experience utilizing AI agents at construction sites spreads, not only single agents but also multi-agent collaboration methods are expected to become active. We look forward to the day we can collaborate with "AI Supervisor Agents" that will solve complex problems at construction sites, going beyond simple task automation. References Heo Jeong-joon (2024) "Developing Practical AI Applications Using LLMs" An Jaeman (2024) "Compound AI System: From AI Agents to AGI," 2024 AI Summit Seoul. Shin Jae-young, Won Ji-seon (2024) "Development Method for an AI Civil Complaint Analysis Service Based on the Needs of Construction Site Workers," Journal of the Korea Academia-Industrial cooperation Society (KAIS), Vol. 25, No. 11, pp. 562-572. Park Tae-woong (2024) "AI Lecture by Park Tae-woong 2025," Hanbitbiz. Department of Future&Smart Construction Research Date2025-06-23 Hit 396
• Development of Ground Compaction Technology for Plant Construction in Freezing Conditions of the Arctic Region Development of Ground Compaction Technology for Plant Construction in Freezing Conditions of the Arctic Region - Ground Compaction and Assessment Technology in a Subzero Environment (-10°C) ▲ Department of Future & Smart Construction Research The Korea Institute of Civil Engineering and Building Technology (KICT) has developed ground compaction technology that ensures stability even in freezing temperature environments as cold as -10°C for energy resource plant construction in Arctic regions. Extraction of unconventional oil in Arctic regions began after 2000, and recoverable reserves of this resource are estimated to be approximately 9 trillion barrels, more than twice the amount of conventional oil, which stands at around 4 trillion barrels. Notably, Canada's oil sands account for 71.6% of the world's total reserves, with daily production reaching approximately 3 million barrels. The Athabasca region in Canada, which contains substantial oil sand deposits, is located at a high latitude, with long winters and temperatures dropping to approximately -20°C during the winter months. The ground undergoes cycles of freezing and thawing, causing repeated surface heaving and settlement. Notably, oil sand regions contain significant amounts of organic soil that is highly sensitive to freeze-thaw cycles, resulting in greater surface heaving and settlement compared to typical ground conditions. To address these challenges, the KICT's Northern Infrastructure Specialization Team (led by Senior Fellow Kim Young-seok) has independently developed ground compaction technology that effectively compacts organic soil even in freezing environments, along with a ground behavior simulation model that takes freeze-thaw cycles into consideration. To assess the freezing-temperature compaction characteristics of organic soil, the team conducted laboratory compaction tests in a freezer chamber capable of temperature control down to -20°C. Canadian organic soil conditions were replicated by mixing silica sand with Canadian organic soil. During this process, researchers developed laboratory compaction test equipment capable of generating compaction curves at -4°C. In addition, a full-scale field compaction test site (8 m width × 8 m length × 3 m depth) was established at the KICT's SOC Demonstration Research Center in Yeoncheon-gun, Gyeonggi Province, Korea. The team replicated Canadian organic soil conditions during winter and evaluated surface heaving and long-term settlement characteristics caused by freeze-thaw cycles in freezing environments reaching approximately -10°C. In conjunction with laboratory compaction tests, field compaction techniques for achieving proper compaction levels in organic soil were verified. Long-term monitoring continues to analyze behavior under repeated freeze-thaw cycles. The team also established a ground behavior simulation model that considers freeze-thaw cycles. This model applies actual measured temperature data to simulate freeze-thaw cycles in backfilled ground, and evaluates earth pressure and displacement. The model was verified by comparing field compaction test measurements with the results of numerical analyses. It offers the advantage of 100% replication of field freezing environment conditions, as it simulates ground freeze-thaw cycles using actual temperature measurements. The research team plans to conduct a field demonstration at the KICT's SOC Demonstration Research Center to verify the performance and practical application of the developed technology. This field demonstration is expected to enable performance evaluations under various conditions that can completely replicate Canadian field conditions by directly burying commercial oil pipelines and establishing systems capable of creating freezing environment conditions. Furthermore, through an international joint study with the Korea Institute of Geoscience and Mineral Resources (KIGAM) and the Canadian resource development company PetroFrontier Corp., the feasibility of demonstrating the developed technology at a field site in Canada is currently under review. The developed technology enables ground compaction even in sub-zero temperatures, securing sufficient construction periods in regions with long winters like the Arctic. It is also expected to minimize surface displacement due to freeze-thaw cycles in regions with abundant organic soil, such as Ukraine's Black Earth (Chernozem) region. "Through this research, we have developed a core technology that will secure construction timeframes for earthwork during winter seasons, which will aid Korean companies attempting to pioneer new markets in future Arctic plant construction," commented KICT President Park Sun-kyu. "As we continue our research and development efforts, we will strive to share these technologies with the related institutions and companies in Korea." This research was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) with funding from the Korean Ministry of Land, Infrastructure and Transport (MOLIT). Department of Future&Smart Construction Research Date2025-03-26 Hit 445
• Building dynamic spatial information for live digital twins in urban areas Building dynamic spatial information for live digital twins in urban areas ▲ Research Fellow Yoon Joon-hee and Research Specialist Kim Ji-eun, Department of Future & Smart Construction Research, KICT From Static to Live Digital Twin Land Digital Twin technology is evolving from Static Digital Twin into Live Digital Twin. In 2003, Dr. Michael Grieves proposed the concept of the "Digital Twin," based on the idea that interaction can be established and intelligence achieved through twinning (or mirroring) of the physical and virtual worlds. Subsequently, with advancements in data transmission, visualization, and platform technologies, digital twin technologies and their platform development have progressed across various domains. From a construction and land management perspective, digital twins can be viewed as a technology that converges, interprets, and visualizes the "shape information" of structures such as buildings, roads, and terrain with "phenomenon information" like population movement, traffic flow, weather changes, and infrastructure transformations, to solve diverse social challenges. Until now, Land Digital Twin has primarily focused on analyzing and visualizing shape information or making it into platforms, consequently limiting its scope of analysis. Now is the time to focus on phenomenon information. A true Live Digital Twin will be completed by combining near real-time phenomenon information with the shape information platform of Static Digital Twin. Dynamic Spatial Information for Live Digital Twin For a Live Digital Twin, the construction of dynamic spatial information is essential. To store, extract, visualize, and analyze information in a digital twin platform, the location and attributes of each piece of information must be acquired and stored. Information with assigned locations and attributes is called spatial information. From the perspective of digital twin development, spatial information needs to be classified into static and dynamic spatial information from the viewpoints of shape and phenomenon. If static spatial information is spatial information that is consistent over the long term, like buildings and roads, dynamic spatial information can be defined as spatial information (Dynamic or Temporary Spatial Information) that exists temporarily from an SOC perspective, such as pedestrians, vehicles, and facility damage, which changes or disappears. Static spatial information can be updated on a cycle of several days to several months. It is generated by the Ministry of Land, Infrastructure, and Transport and local governments in accordance with laws, and also is independently generated and used by companies like Google, Naver, and Kakao. On the other hand, dynamic spatial information has an update cycle of several minutes to several days. Currently, the spatial and object targets of dynamic spatial information are vehicles on major roads, with information provided and updated through CCTV, probe cars, and driver reports, which have limitations in terms of their update cycles and spatial recognition range. However, recent developments in AI-based image processing, IoT, drones, Urban Air Mobility (UAM), and satellite technologies are making it possible to overcome such limitations. KICT's Dynamic Spatial Information Construction Technology The Korea Institute of Civil Engineering and Building Technology (KICT) has been leading a project titled "Development of Dynamic Thematic Map Construction Technology Based on Fixed/Mobile Platforms for Next-Generation Digital Land Information," with a total budget of KRW 18.2 billion, since 2022. This project is one of the four core initiatives of the Digital Land Information Technology Development Program, managed by the Korea Agency for Infrastructure Technology Advancement (KAIA). The KICT has been developing technologies to generate and update dynamic spatial information in near real-time and represent it accurately. This project defines dynamic spatial information as information occurring in urban living SOC, including moving objects and changing phenomena. Its goal is to develop dynamic information thematic map construction technologies through continuous near real-time detection and tracking using fixed sensors (CCTV, Wi-Fi, etc.) and mobile sensors (drone stations) to solve various social problems. While CCTV allows 24-hour monitoring but has a limited area of coverage, drones (stations) can cover wider areas but cannot monitor 24/7. This project aims to merge the advantages of both platforms for urban area monitoring. Figure 1 illustrates the concept of urban monitoring based on ground-fixed and airborne mobile sensors. The project consists of three main core technologies: "Development of Dynamic Information Collection Technology Based on Fixed Platforms," "Development of Dynamic Information Collection Technology Based on Mobile Platforms," and "Development of Dynamic Information Analysis, Prediction, and Representation Technology." These are further divided into a total of six core sub-technologies, as shown in Figure 2. In "Development of Dynamic Information Collection Technology Based on Fixed Platforms," specifically within the "Development of Heterogeneous Sensor Linkage and Mobile Information Collection Technology" section, the research focuses on the real-time detection and tracking of mobile objects, which is achieved by integrating object detection and tracking technologies with fixed sensor equipment such as CCTV, Wi-Fi, and Bluetooth. This involves analyzing the environment of fixed platforms and developing methods for acquiring and collecting sensor information, creating data models for transmitting and storing mobile object location data using heterogeneous sensor data, interconnecting different sensors, recognizing and classifying mobile objects, and extracting the location information of mobile objects in heterogeneous sensing environments. In the "Development of Continuous Time-Series Mobile Object Information Tracking Technology Based on Fixed Platform Linkage" section, the research advances the development of mobile object data models for continuous location tracking. This is achieved by collaborating with fixed sensor equipment to monitor specific areas and developing technologies for seamless location handover between homogeneous and heterogeneous sensors. The goal is to enable the continuous tracking of mobile objects' time-series location data within urban environments. In the part titled "Development of Technology for Collecting Information Using Fixed Platform Heterogeneous Sensor Integration and Mobile Object Data," the research discusses how sensor devices that are fixed in place, such as CCTV, Wi-Fi, and Bluetooth, can be utilized to detect and track objects in real time using object detection and tracking technologies. The research includes analyzing fixed platform environments, developing methods for acquiring and collecting sensor data, and creating models for transmitting and storing mobile object location data using heterogeneous sensor data. Additionally, the project aims to develop technologies for the recognition and classification of mobile objects using heterogeneous sensors, as well as for extracting the location information of mobile objects in heterogeneous sensing environments. The "Development of Dynamic Information Collection and AI Learning Data Construction Technology" section focuses on collecting dynamic information in urban areas and constructing AI learning datasets. To achieve this, drone/mobile platforms and operation systems for dynamic information collection are established, taking into account the characteristics of each test bed region. In addition, technologies for converting learning data, automatic classification, and the automated construction of multi-dimensional dynamic information datasets are developed by topic. In the "Development of Knowledge/Learning-based Dynamic Information Recognition Technology" section, the research aims to develop knowledge and learning-based dynamic information recognition and integration algorithms using data collected from mobile platforms. The goal is to enable collaborative and continuous object recognition between fixed and mobile platforms. The research involves developing dynamic information data integration algorithms that can account for spatio-temporal changes, as well as technologies for object-specific dynamic information recognition, classification, and situation detection. Furthermore, the research is focused on creating collaborative object observation technologies between fixed and mobile platforms to visualize the outcomes of these efforts. Finally, the "Development of Dynamic Information Analysis, Prediction, and Representation Technology" part analyzes the results from the previous two parts and constructs dynamic thematic maps based on these findings. The "Development of Dynamic Information Analysis and Prediction Technology Based on Movement Context Information" section aims to generate movement context information by linking object-level movement information and static data collected from fixed/mobile platforms, with the goal of applying AI to movement analysis and prediction. To achieve this, the research is developing technologies for: linking static data and data mining for movement context information generation, creating movement time-series pattern information and context information, and applying AI technologies for movement analysis and prediction based on context information. In the final "Development of Dynamic Thematic Map Construction and Update Technology" section, utilizing the previously developed dynamic information, the research seeks to construct and update user-customized dynamic thematic maps. This involves identifying dynamic thematic map service models from public and private sector perspectives, developing 2D/3D visualization technologies for multi-dimensional dynamic information including location, time, and status, and creating technologies for user-customized dynamic thematic map construction and updates. Figure 3 illustrates an example of a dynamic thematic map. The project has in particular focused on establishing early test beds from the first year to successfully demonstrate the project, verifying annual achievements. Leveraging the existing experimental infrastructure from previously completed intelligent crime prevention and immersive disaster research units at the KICT, the project aims to minimize research and development risks by working in close collaboration with Anyang City as a local government demonstration site. This includes establishing drone/operation platforms within the Anyang City test bed and acquiring actual urban data such as CCTV footage and IoT sensing data. In addition, for dynamic thematic maps, the project is identifying and implementing user-oriented demand-based dynamic thematic maps through regular commercialization consultation meetings involving key public institutions, local governments, and private sector stakeholders. Department of Future&Smart Construction Research Date2024-12-27 Hit 949
• The Adoption of AI in the Construction Industry During the AX Era The Adoption of AI in the Construction Industry During the AX Era ▲ Senior Researcher, Won Ji-sun, Department of Future & Smart Construction Research, KICT Prologue Following the release of ChatGPT, which surpassed 100 million users within just two months, AI smartphones with on-device AI and generative AI capabilities are also causing artificial intelligence, which once felt like a technology that was far off in the future, to permeate our daily lives. Beyond daily life, applying AI to business is becoming a necessity rather than an option. Whenever new AI technologies emerge, we find ourselves constantly considering how to apply them to our work, and how to formulate strategies for the future. We are living in an era in which we must continually consider the implications of AI advancements for our professions. As AI technology progresses rapidly, the AI Transformation (AX) is becoming a present-day issue rather than a future concern (ETNews, 2024). In the upcoming AX era, what tasks do construction industry professionals wish to apply AI technology to, and what difficulties are they facing in the adoption process? The Korea Institute of Civil Engineering and Building Technology (KICT) conducted a survey in 2022 to gauge the industry's perception and demand for the adoption of construction AI as part of its major project (Research on Smart Construction Technologies to Drive the Future Construction Industry and Create New Markets, 2022-2023). Although the survey results may not accurately reflect the current demand due to changes in the environment, we hope that sharing some of its findings will help in setting future directions and determining appropriate responses to the technology. Survey Overview and Respondent Characteristics The overall survey items covered ① the current status and plans for AI adoption at the respondent's affiliated organization, ② perceptions of AI adoption in the construction sector, ③ demand for AI adoption in the construction sector, and ④ barriers to AI adoption in the construction field and measures for creating an ecosystem. In this article, we focused on analyzing item ③, which surveyed the construction tasks that respondents wanted to prioritize for AI technology adoption, and item ④, which examined the long-term measures needed in the construction industry. Items ① and ② were excluded, as we expect the answers to items ① and ② to change significantly depending on the AI market situation. The survey was conducted targeting workers in the construction industry, and was participated in by a total of 107 respondents. Regarding having experience utilizing AI technologies for construction work, 49.5% had such experience while 50.5% did not, an almost equal ratio. In terms of the respondents' affiliated organizations, 29% were from design firms, 22.4% from corporations/public corporations, and 16.8% from academia/research institutes. Regarding job responsibilities, design and construction work accounted for 32.8% and 21.5%, respectively, together representing more than half of the respondents. Approximately 82% of the respondents had more than 10 years of experience in the construction industry, and the facility areas they were in charge of were buildings (42.1%) and roads (34.6%) respectively, together comprising a significant portion. Current Demand for AI Adoption in the Construction Sector To assess the demand for AI adoption in the construction sector, we provided a list of tasks in each construction phase where AI could be applied, along with examples of AI applications for those tasks. Respondents were asked to select the tasks they considered most urgent for AI technology adoption, in the order of priority. In this article, statistics on the top priority tasks and the results of a demand analysis according to respondents' characteristics are selectively explained. Planning and Design Phase The demand in the planning and design phase was surveyed based on the eight tasks shown in Figure 2. A comparison of demand between all respondents and those in charge of planning/design tasks, as well as the results of a demand analysis according to whether they have experience utilizing AI or not, is as follows: Both the group of all respondents and the group in charge of planning/design tasks showed high demand for "design analysis and interpretation" to derive optimal design solutions and extract design characteristics, "duration and cost estimation" to predict approximate estimates, and "design planning and plan establishment," like generating various design alternatives. The two groups assigned the same priorities to 8 specific tasks. Comparing the demand based on AI utilization experience, the AI-experienced group showed a noticeably higher demand for "design planning and plan establishment" than the AI-inexperienced group. This is likely due to their practical experience with AI-based design automation solutions, reflecting higher expectations around the benefits of adoption. Construction Phase The demand in the construction phase was surveyed based on the five tasks shown in Figure 3. A comparison of demand between all respondents and those in charge of construction tasks, as well as the results of an analysis of demand according to years of service in the construction field, is as follows: All respondents and the construction task group showed high demand for the adoption of AI in "safety management," such as accident prediction and disaster case classification. and "process management," like process optimization. Notably, the construction task group showed about 15% higher demand for AI adoption in "safety management" than the average of all respondents. This trend is attributed to the increasing importance of construction site safety, highlighted by laws such as the Serious Accident Punishment Act, leading to a heightened perceived need for AI-based safety management technologies in the field. When we compare demand based on years of service in the construction industry, those with less than 5 years of experience showed a relatively higher demand for AI adoption in "quality management," while those with 5 to 10 years of experience showed a higher demand for AI adoption in "progress management" compared to other groups. Maintenance Phase The demand at the maintenance phase was surveyed based on four tasks depicted in Figure 4. The comparison of demand for AI adoption in their tasks between all respondents and maintenance task personnel, as well as the analysis of demand for AI adoption in their tasks based on years of service in the construction industry and whether they have experience using AI, are as follows: Both all respondents and the maintenance task group commonly identified "inspection and diagnosis," which deals with damage detection and condition grade assessment prediction, as the most urgent task for AI technology adoption. The maintenance task group showed a higher demand for AI adoption in "repair and reinforcement," which predicts repair methods, costs, and timing, compared to "continuous monitoring,” such as structural condition change monitoring, indicating a difference in perspective between the groups. The demand for "preventive maintenance," such as creating deterioration models and predicting aging, was the lowest. Looking at the demand for AI technology adoption according to years of service in the construction industry, the group with more than 15 years of experience showed the highest demand for "continuous monitoring." Interestingly, the group with 5-10 years of experience showed a higher demand for AI technology adoption in "preventive maintenance" compared to "repair and reinforcement." Examining the technology demand based on AI technology utilization experience, those with AI experience responded that "continuous monitoring" was the most urgent task area for AI adoption, while those without experience said "inspection and diagnosis" was the most urgent area for AI adoption (Won Ji-seon, 2024). Barriers to AI Adoption in Construction and Measures to Create an Ecosystem To assess the barriers to introducing AI in the construction sector and prepare measures for facilitating AI adoption in the future, opinions were surveyed by dividing respondents into groups with and without AI technology utilization experience. The difficulties in introducing and utilizing AI were surveyed in the order of "data acquisition and quality issues," "lack of AI-related personnel," and "lack of construction-specific foundational technologies" for both all respondents and the AI utilization experience group. A survey on measures to overcome AI adoption barriers and promote AI adoption in the construction field that was conducted on a group with AI development experience revealed many opinions on the "establishment of AI infrastructure, including data openness.” In addition, tasks such as "nurturing AI personnel," "expanding awareness of AI utilization," "improvement of regulations and establishment of regulatory systems" and "support for AI-related R&D" were identified (Shin Jae-yeong et al., 2023). Epilogue This article examined the current demand for construction tasks requiring the introduction of AI technology based on the opinions of 107 construction industry workers. Today, the construction industry is facing new changes with the emergence of generative AI. It is said that the future of technology is determined by how familiar and useful it is to people rather than its innovativeness. To utilize AI technology valuably and usefully in business, it is necessary to first identify the tasks with which help is required, and which problems to solve. We hope this data will help construction industry workers understand their needs and devise their own strategies. ――――――――――――――――― References • Let’s Lead the AI Transformation (AX) Era, ETNews (January 1, 2024), https://www.etnews.com/20240101000072. • Survey Report on Perception, Demand, and Ecosystem Creation Measures for AI Adoption in the Construction Industry, KICT (2022) • Current Perception and Research Trends of AI in Facility Maintenance, Won Ji-seon (2024), KACEM News, Vol. 242. • A Study on the Perception of Practitioners for Facilitating AI in the South Korean Construction Industry, Shin Jae-yeong, Won Ji-seon (2023), Journal of the Korea Academia-Industrial Cooperation Society, Vol. 24, No. 6, pp. 386-399. Department of Future&Smart Construction Research Date2024-06-27 Hit 1694
• Recent Trends in Artificial Intelligence Research for Facility Maintenance and Management Recent Trends in Artificial Intelligence Research for Facility Maintenance and Management ▲ Senior Researcher Won Ji-sun, Department of Future & Smart Construction Research, KICT Prologue The global artificial intelligence (AI) market in the construction sector is predicted to grow at an average annual rate of 35%, reaching KRW 2.33 trillion by 2023 (Market Research Future, 2020). At the same time, the Korean AI market is anticipated to reach KRW 1.9 trillion by 2025, with an average annual growth rate of 15.1% for five years from 2021 (IDC Korea, 2022). In recent times, the construction industry has adopted or has been actively considering adopting various AI technologies across the design, construction, and maintenance phases. The adoption of AI technologies has shown positive effects in the construction industry, including shortened construction durations, cost savings, improved safety, and enhanced quality (Lee, 2020). The adoption and utilization of AI technology are recognized as essential strategies, not optional choices, for enhancing corporate competitiveness. At the national level, there is a need for research strategies and direction-setting in the field of AI technology that align with the role of the public sector in securing the competitiveness of AI technology in construction. This article aims to introduce a portion of the research conducted (Won et al., 2022) in the field of future AI technology for facility maintenance, focusing on numerical data and case studies, to help establish research directions and preparations. AI Research Trends Seen Through Numbers Over the past five years (2016-2021), I've analyzed a total of 33 documents, including research papers and reports, related to AI technology development in the field of facilities maintenance and management. During the document collection, basic search keywords, such as "artificial intelligence," "maintenance," "machine learning," "deep learning," and "convolutional neural network," as well as various model names commonly employed in research, were used. I've analyzed research trends based on the 41 collected AI application cases, considering four perspectives: 1. Purpose of utilizing AI, 2. Targeted facilities, 3. Collected raw data, and 4. Types of learning data. Through my analysis of documents from the perspective of utilizing AI, two main types of AI technology usage were identified: direct utilization of AI technology for maintenance works, and utilization of AI in the intermediate stage for data collection and processing for learning purposes. The research areas that directly employ AI in maintenance work were further categorized into Inspection and assessment, Continuous measurement, Repair and reinforcement, and Aging prediction. The research areas in which research is active are as follows, and are listed in order of prominence: Inspection and assessment (62%), Building learning data for AI (17%), Continuous measurement (7%), Repair and reinforcement (7%), and Aging prediction (7%). To summarize, the current status of research regarding the five purposes of utilizing AI is as follows: 1. [Inspection and assessment] AI applications for inspection and assessment primarily focus on damage detection, such as crack detection using facility inspection photos. Local governments and construction corporations are increasingly adopting automated inspection technologies utilizing unmanned vehicles, such as drones and robots, for inspections in hard-to-reach areas. The development of classification models, primarily around concrete crack detection, is the most prevalent trend. Additionally, the technology is being expanded to include other types of damage beyond cracks, as well as techniques for quantifying damage location, size, and area. 2. [Repair and reinforcement] Regarding the application of AI for repair and reinforcement, research is being conducted with the purpose of training AI with repair and reinforcement data to predict maintenance methods and costs, as well as to predict repair timings exceeding the criteria by using time-series accumulated images of visual inspection grids. 3. [Continuous measurement] Regarding the application of AI for continuous measurement, research has primarily been focused on predicting changes in the condition of facilities and detecting real-time defects for immediate response. This research utilizes accelerometer sensor data and IoT sensor data to detect damaged locations or measurement anomalies, with the purpose of managing performance changes and risks. 4. [Aging prediction] In the application of AI for aging prediction, research is being conducted primarily on creating concrete degradation models or estimating remaining service life based on accelerometer data and structural health data, and utilizing this information for preventive maintenance. 5. [Building learning data for AI] In building data for AI learning, research is being conducted during the data collection and preprocessing stages with the purpose of creating learning data that is currently lacking, such as accelerometer data and crack images, or enhancing low-resolution images to high-resolution ones. The target facilities in AI application research, ranked in descending order, have been bridges (58%), concrete structures (22%), road facilities with a focus on road pavement/surfaces (15%), and buildings (5%). In terms of the types of learning data for AI, images (56%) outnumbered text (44%). Among the detailed types of text data derived from collected raw data, it was observed that measurement data obtained from equipment, database data acquired from system databases, and documents such as inspection reports were utilized, in that order. Of the 33 collected documents that specified data collection methods, it was found that 5 (15%) used retained data, 9 (27%) used publicly available data, and 19 (58%) collected data directly through measurements and crawling, among other methods. AI Research Trends Seen Through Cases Through the analysis of 34 previous research cases related to inspection and assessment, repair and reinforcement, continuous measurement, and aging prediction, this study presents the main research status for each specific maintenance and management work type, utilization purposes, data utilized, and representative research cases. Epilogue We have examined trends in AI research in the field of facility maintenance through numbers and cases. In terms of works where AI is applied, the area of inspection and assessment stands out as the most active and technologically mature field within the maintenance domain. It is expected that the adoption of AI will accelerate, particularly for facilities that are difficult to inspect visually and are dangerous to access. Furthermore, as maintenance technology in Korea transitions towards proactive and preventive maintenance systems in response to aging infrastructure and facilities, there is a growing demand for AI research in aging prediction. In terms of data, image-based research is currently the most active, with text-based research acquired through measuring equipment also being quite prevalent. With recent advancements in natural language processing technology, the expansion of text-based research utilizing construction documents such as inspection reports in the future is anticipated. Many documents highlight the limitations of insufficient AI learning data in their research. Given that this significantly impacts the efforts to secure AI performance, it is expected that the establishment of specialized AI learning datasets for the field of maintenance and research on data quality will become increasingly important. Department of Future&Smart Construction Research Date2023-10-11 Hit 988
• Development of Visualization Technology for Building Energy Information Based on IndoorGML Development of Visualization Technology for Building Energy Information Based on IndoorGML ▲ Research Fellow Choi Hyun-sang, Department of Future & Smart Construction Research, KICT Prologue In the representation of indoor spaces used in the construction of indoor spatial information, international standards such as IFC (Industrial Foundation Classes), CityGML (City Geographic Markup Language), and IndoorGML (Indoor Geographic Markup Language) can be applied. There are two ways to construct indoor space data using these standards: the first is a direct construction method using authoring programs, which allows for detailed representation but involves a significant amount of time and cost. The second method involves creating data by converting data that are already standard or are used in practice, which is effective in reducing time and cost. Thus, this study aimed at developing a Revit Plug-In based on BIM to extract core indoor spatial information object from sample models, convert them to IndoorGML and integrate them with data visualization technology to develop the supporting technology for the utilization of indoor spatial information. Theoretical Considerations of IndoorGML IndoorGML is a data model for expressing and exchanging indoor spatial information, which was developed by the Open Geospatial Consortium (OGC), an international standardization organization for spatial information. It is a data standard in GML format based on XML (eXtensible Markup Language) schema. IndoorGML was developed to support the requirements for indoor spatial data services, and is defined based on a cell space model. IndoorGML focuses on the expression of the geometric relationships and topology (topological relationships) information of indoor spaces, rather than the detailed representation of indoor objects such as building components or furniture. In IndoorGML, the smallest and most basic spatial unit that constitutes a building is called a cell space, and a building is considered a series of cell spaces. To represent this cell space model in detail, IndoorGML defines the following four items: Cell Geometry Topological Relationship between Cells Meaning of the Cell Multi-Layer Spatial Model Based on the four definitions mentioned above, IndoorGML can ① represent the characteristics of indoor spaces, and ② provide spatial reference information about the topographic features located within indoor spaces. Figure 1 shows the geometry options provided by IndoorGML. It displays three options for geometric representation in IndoorGML, and the meanings of each option are as follows: Option 1 : (External Reference) Instead of explicitly representing geometry in IndoorGML, it can be expressed solely through external links to objects defined in other datasets, such as CityGML. OOption 2 : (IndoorGML Geometry Information) When including geometric representations for cell spaces in IndoorGML, 3D spaces are represented as GM_Solid, and 2D spaces (walls) are represented as GM_Surface according to the definition in ISO 19107. Openings (e.g. doors, windows, etc.) are also included in this case. OOption 3 : (No Geometry) IndoorGML document does not include geometry information for cell spaces (spaces can be represented solely by Nodes). Geometry Rules for IndoorGML Conversion The geometry rules for the key objects that constitute IndoorGML are based on the modeling rules presented in the SIG3D "Modeling Guide for 3D Objects Part 1: Basics (Rules for Validating GML Geometrics in CityGML)" technical document. Among the regulations in the aforementioned technical document, the implementation rules for representative objects that are most closely related to this study are as follows: gml : LinearRing: The geometry composing the objects that make up the building is comprised of a single polygon boundary, i.e. a LinearRing (Rs) (Figure 2). gml: Polygon: A polygon (S) is represented as a set of planar LinearRings (Rs). gml : MultiSurface: The MultiSurface used to visually represent the surface objects (M) that make up a building is represented as a collection of unstructured polygons (S), i.e., M={S1, S2, Sn}. gml: The geometry of a 3D object is defined as a collection of polygons that are composed of multiple surface objects (Multi-Surface), and errors can occur depending on the composition of the polygons. Table 1 shows examples of correct and incorrect cases when constructing indoor objects. Development of IndoorGML Plug-In Based on Revit Software (1) Design of Revit Data Conversion Process Autodesk's Revit software, which is commonly used to create 3D BIM models, provides a range of 3D modeling features that support accurate input in terms of visualization and geometry, as well as tools to input and manage relationships between constituent objects. In this study, the Room Schedule and Door Schedule functions provided by Revit were used as a basis, and the CellSpace (Node) and Transaction (Edge), which are core objects of IndoorGML, were constructed based on the connection information entered between the spaces during building design. However, if Room/Door Schedule is missing in the initial BIM modeling process or is omitted due to worker error, it must be checked and corrected through a pre-validation process. Figure 3 shows the data conversion process applied in this study. (2) How to Use Revit's Room Objects, and Rules for Handling Virtual Spaces To extract CellSpaces in IndoorGML using Room objects created in Revit, it is necessary to first check whether the Room object has been input into the Revit model. Figure 4 shows that if a Room object has been input, it is displayed on the screen with crosslines, and that even irregular spaces can be configured as Room objects. In the design of typical buildings, only spaces composed of actual structures (walls, columns, floor surfaces, ceiling surfaces, etc.) are represented. However, in IndoorGML, an indoor space information, it is necessary to divide virtual indoor spaces for large spaces such as auditoriums or banquet halls, as well as narrow and long corridors with changing directions. For this purpose, preprocessing of virtual spaces is required before converting to IndoorGML, and setting and modifying rules for processing virtual spaces is necessary. In this study, additional functions were developed based on the features provided by Revit for processing virtual spaces. (3) Main Features and Achievements of Revit SW-based IndoorGML Plug-In In the Revit SW, it is common to create Room and Door Schedules during the BIM modeling process. However, there may be cases in which they are omitted due to human error or spatial constraints, so it is necessary to check them in advance and make corrections as needed. Figure 5 shows a feature provided by Revit that allows the user to check Room Tags and missing information. Then, when converting Revit data to IndoorGML data using the "IndoorGML Exporter" menu, a verification process is also carried out to check for any missing information. Once the verification of the Revit data that serves as the source of IndoorGML is complete, the user can selectively convert only the desired floors or the entire building into a single IndoorGML file. Figures 7 and 8 show examples of the conversion of Main Buildings 1 and 2 of the Korea Institute of Civil Engineering and Building Technology (KICT). (4) Development of IndoorGML-based Building Energy Information Visualization System In this study, we developed a 3D system that can visualize building energy management by assigning representative values for each spatial unit based on measured values by room and location in Main Building 1 of KICT that was investigated through the aforementioned process, as well as values obtained from the survey. Figure 9 shows the process of integrating the results of a user satisfaction survey program for the building, KBOSS, into indoor space units (left), and examples of floor-by-floor visualization (right). Epilogue This study was performed to secure the core technology for integrating and managing detailed energy data for individual building units and occupant satisfaction survey results in a format that is compliant with the international spatial information standards, which is necessary for developing the technology for energy inspections of metropolitan-scale buildings. Through this study, an IndoorGML data authoring tool was developed and applied to store and represent energy-related information investigated for KICT at the minimum space unit (room) level. It is expected that the results can be utilized as a database and operational technology for micro-level building energy inspection information in the future implementation of carbon reduction policies, which are an important part of building energy monitoring and management on a national scale. Department of Future&Smart Construction Research Date2023-02-27 Hit 859
• ISO 19650-based BIM Information Management Framework ISO 19650-based BIM Information Management Framework ▲ Senior Researcher Won Ji-sun, Department of Future & Smart Construction Research, KICT Prologue In this "Digitize or Die" era, digital transformation is recognized as an essential strategy for corporate survival, and is accelerating across all industries. The construction industry is responding to paradigm shifts through the spread of smart construction technologies such as Building Information Modeling (BIM) adoption, construction machine automation, and the activation of Off-Site Construction (OSC). In July of this year, the Ministry of Land, Infrastructure and Transport (MOLIT) announced the "S-Construction 2030” plan, which aims to achieve "digitalization and automation of the entire construction process by 2030." It presents three promotional tasks for achieving this goal: digitalization of the construction industry, advancement of the production systems, and promotion of the smart construction industry. Of these, the detailed plan for realizing the digitalization of the construction industry specifies the organization of the BIM system and the phased expansion of projects subject to mandatory BIM application. Other countries, including the UK, Denmark, and Ireland, have also introduced the concept of digitalization into their existing BIM roadmaps and are redesigning them as national digital transformation strategies or digital twin strategies. BIM is now recognized as an essential strategic tool for digital transformation. Upon examination, it is evident that ISO 19650 is being actively adopted. ISO 19650 is a BIM information management framework that standardizes the process and information requirements for BIM information procurement across the life cycle of a construction project, and was established in 2018. This international standard was developed by adding digital information management concepts to the UK’s BIM standards (BS 1192 series), which was previously used as the global standard during the early phases of BIM adoption. The UK, Europe, and Australia have designated the ISO 19650 original text or translation as their national BIM standard, while countries like Singapore, Hong Kong, and Saudi Arabia are revising their national BIM standards to include ISO 19650. Many countries are now mandating ISO 19650 certification as a prerequisite for bidding on public construction projects or offering incentives, and more companies in Korea are obtaining ISO 19650 certification to demonstrate their global-level BIM information management technology and capabilities. There is a growing trend of the active utilization of ISO 19650 as part of a BIM-based digital transformation policy. Moreover, as a company's ISO 19650 certification and compliance capacity has become a measure of competitiveness, it is necessary to consider the introduction of ISO 19650 at the national level in Korea. Thus, we aim to propose strategies and methods for introducing ISO 19650 in Korea. In this study, we adopted an approach that reflects the key concepts of ISO 19650 in accordance with the situation in Korea. Our research involved three steps. First, we investigated the current status of ISO 19650 adoption in other countries, and derived the key components of the BIM information management framework by examining international standard documents. Second, we analyzed the software, platforms, and other support tools that enable ISO 19650 adoption, and selected the main functions that need to be implemented for practical application. Third, based on the key components of ISO 19650 and the main functions of ISO 19650 support tools, we proposed an ISO 19650 utilization model and suggested ways to introduce it in Korea. Stages 2 and 3 can be understood as a process of scanning multiple buildings from an urban/regional perspective based on appropriate indicators (whole-building level identification), while Stages 4 and 5 can be understood as a process of closely examining the scanned buildings in detail from a building component perspective (system level diagnostics). In this study, we would like to introduce the data-centric checkup technique of building energy performance that corresponds to Stages 2 and 3 in this context. Current Status of ISO 19650 Adoption in Other Countries Generally, national BIM roadmaps utilize BIM maturity models to establish phase-specific goals for BIM adoption levels and situations. Many countries have already been utilizing the BIM maturity model defined in the UK BIM roadmap (British Standards Institution B/555), which was announced in 2011, as a global standard. In the BIM maturity model of the UK, Level 0 is set in an environment centered on documents such as 2D drawings and text, Level 1 is set in an environment where 2D drawings and 3D data files are used concurrently, Level 2 is set in a discipline-specific BIM model environment, and Level 3 is set in an integrated web-based BIM environment that centrally manages data through a single model. The UK is actively utilizing ISO 19650 to attain Level 2, and is preparing a digital transformation roadmap for attaining Level 3. Currently, most countries are in the Level 2 adoption or activation phase. Many countries are in the process of adopting ISO 19650, as shown in Table 1. Thus, the adoption of ISO 19650 is recognized as an essential requirement for attaining BIM Level 2. The ISO 19650-1 established in 2018 presents the maturity levels of digital information management in each phase as a concept of "stage." The types of data, such as 2D, 3D, and BIM, covered in the UK BIM maturity model have been changed to concepts such as structured, unstructured, BIM, and server-based BIM, and the concept of Common Data Environment (CDE) has been subdivided into the file- and model-based CDE forms and the big data-based CDE forms. Digital information management maturity for each phase is divided into three information management stages along the horizontal axis, and is composed of four layers (standard, technology, information, industry) that represent the major information management perspectives along the vertical axis. In terms of information management perspectives according to standards, Stage 1 is defined as information management based on existing national standards for handling structured and unstructured data, Stage 2 as information management based on ISO 19650 standards for handling shared BIM models, and Stage 3 as information management based on future standards for handling server-based BIM models and structured/unstructured big data. The current stage is Stage 2, and to achieve the corresponding level, information management based on ISO 19650-1 and 2 is required. Deriving Key Components of BIM Information Management Framework Through Analysis of ISO 19650 To achieve the goals aligned with the BIM maturity level or digital information management maturity level, it is important to specify the national-level BIM standards that must be complied with at each phase. Specifically, there are BIM guidelines, BIM classification systems, contracts related to information procurement and LOD standards, as well as BIM maturity assessment methodologies. The BIM Information Management Framework is a standardized system that supports workflows and data acquisition to generate, utilize, and manage BIM data in an integrated digital construction environment throughout the construction life cycle. BIM standards related to the BIM Information Management Framework include BIM standard classification, building SMART International's IFC, IDM, IFD, and COBie. ISO 19650 covers processes in the digital collaboration system such as subject-specific information requirements, digital model deliverables, workflows, information management plans, CDE, etc. from the perspective of comprehensive use of these open standards. The currently published ISO 19650 series is as follows: ISO 19650-1(2018) : Concepts and Principles for Information Management Using BIM ISO 19650-2(2018) : Information Management Using BIM in the Delivery Stage ISO 19650-3(2020) : Information Management Using BIM in the Operational Stage ISO 19650-4(2022) : Process and Standards for Information Exchange ISO 19650-5(2020) : Security Management During Information Management Using BIM ( 1 ) ISO 19650-1 (2018): Concepts and Principles for Information Management Using BIM ISO 19650-1 contains the concepts and principles of an information management framework for BIM collaboration throughout the construction life cycle. Information management is defined as "the process of supporting the production and management of information over the entire construction asset life cycle." The key components of the BIM information management framework are: ① specification of information requirements, ② planning for information delivery, and ③ delivery of information, which support a collaborative environment to enable the consistent delivery of information that varies by project, stakeholder, and purpose through a coherent process and delivery system. In the project delivery phase and operational phase, an information procurement plan is established based on the information requirements of the participants and contractors. In addition, it has the flow in which deliverables reflecting this, such as PIM (Project Information Models) and AIM (Asset Information Models), are delivered and approved. For effective information management, the setting of responsibilities, authorities, and scope of work is crucial, and pertinent functions should be assigned during the project and asset management period. The responsibility assignment items must be specified in the contract document (e.g., through a Responsibility Matrix) to ensure that a person with “AIM approval competency” is designated for asset management and a person with the information standard, process, and CDE configuration competency of the project is designated for project delivery. ( 2 ) ISO 19650-2 (2018): Information Management Using BIM in the Delivery Stage ISO 19650-2 sets information requirements during the project execution phase, and defines a collaborative environment and process for lead appointed parties and appointed parties to efficiently produce information. The information entities of the project delivery phase are set as the appointing party, lead appointed party, and appointed party. The information management process as well as function and standard requirements for each entity are presented for each project delivery phase. A total of eight information management functions in the project delivery phase are defined, and the detailed information management processes for each entity are specified in each section of Chapter 5 in ISO 19650-2 (5.1 Evaluation and requirements → 5.2 Bid announcement → 5.3 Bidding participation → 5.4 Contracting → 5.5 Resource mobilization → 5.6 Collaborative information production → 5.7 Information model delivery → 5.8 Project completion). In this study, ISO 19650-1 and 2 were analyzed to identify the key components of the framework, including specifications related to information management entities, requirements, processes, deliverables, and roles, and were divided into seven components as shown in Table 2 (1. Information Requirements, 2. Information Delivery, 3. Information Management Entities and Roles, 4. Workflows, 5. Information Procurement Plan, 6. Information Management Level, and 7. CDE). Deriving Key Functions through Analysis of ISO 19650 Practical Application Support Tools To apply the ISO 19650 component concept in practice, it is necessary to identify the actually implemented functions and interfaces. According to a survey of the software, websites, platforms, and other tools that support ISO 19650, it was found that the Plannerly platform from the United States is a representative tool that faithfully incorporates the ISO 19650 concepts. However, there are many tools, like US BEXEL, that only partially support ISO 19650 concepts, such as information delivery and CDE concepts, and open BIM formats such as IFC, BCF, and COBie. ( 1 ) The US: Plannerly Plannerly is a BIM information management platform that provides integrated support for the appointing party (project owner), designing party (AE), lead appointed party (contractor), and the appointed party (subcontractor) to plan, manage, and validate BIM requirements in one place. It is designed to facilitate the easy and efficient use of BIM standards, requirements, processes, and regulations in accordance with ISO 19650, and provides an environment in which all construction stakeholders can collaborate on information and processes without disruption on a single site. Its interface features ISO 19650 templates (OIR, PIR, EIR, AIR, BEP, etc.) based on the UK BIM Framework guidelines and workflows to enable easy and consistent operations. The platform also incorporates the CDE concept to enable the centralized generation, storage, and management of information. It is largely comprised of six modules: Plan, Scope, Contract, Schedule, Track, and Verify. ( 2 ) The US: BEXEL Manager BEXEL Manager is software that supports digital workflows in an open BIM environment according to ISO 19650, and provides a collaborative environment to manage the PIM and AIM information delivery models in a CDE environment. It supports open standard formats such as IFC standards, MVD, BCF, and COBie. Based on an analysis of these two support tools, it was determined that the key factors to consider when introducing them to Korea are whether they support a BIM-based workflow, including BIM contract and requirements management, BIM task performance, BIM data verification, collaboration, information requirements definition, information procurement plan establishment, information management level setting, and open BIM standard formats. The main functions to benchmark are derived in Table 4 based on such analysis results. To create building-level screening indicators, the dataset collected at the building registry level should be matched and integrated (Figure 2, ② Data Preprocessing). However, since publicly collected data is generated for different policy and administrative purposes, there usually is no unique key to link and match the building registry information. Therefore, the location information (latitude and longitude) and address information (street number, dong, ho or suite number) of each data must be processed and linked to match the resolution of the building registry. This task requires string processing technology for non-standardized address and location information, which is quite difficult and requires a substantial budget and time. Approaches to Introduce the ISO 19650-based BIM Information Management Framework to the Republic of Korea The ISO 19650 utilization model is a conceptually defined model that integrates the key components of a digital-based BIM execution workflow and data procurement framework for BIM information management, from a user perspective, to enable unified utilization. The ISO 19650 utilization model was constructed based on the main components of the BIM information management framework derived through the analysis of ISO 19650 and the main functions derived through the analysis of ISO 19650 support tools. The ISO 19650 utilization model consists of six modules, as shown in Figure 4. Module 1 is Standards, which signifies Open BIM standard for exchanging and distributing BIM data and Standards for defining the BIM information management operating system. Module 2 is Requirements, which functions to set information requirements, information management entities and roles, and information procurement plans in project phases such as design and construction and facility operation phases. Module 3 is Workflows, and it is designed to define and manage detailed BIM processes for each project delivery and operational phase. Module 4, Deliverables, defines and manages PIM and AIM data, which are information delivery outputs. Module 5 refers to the CDE environment for collaboration and sharing. Modules 2 and 3 pertain to the process area, while modules 4 and 5 consist of the data area created, shared, and saved according to the process. Modules 2 through 5 need to be operated to achieve a sequential flow. Module 1 is used as a criterion for data creation, and module 6 serves as an interface where the BIM information management entity utilizes modules 1 to 5. The concept of each module can be provided in the form of specifications, such as standards and guidelines, or in the form of platform functions. We propose the following implementation plan and future tasks to apply the ISO 19650 utilization model in practice. First, in order to establish Level 2 of BIM in Korea, it is necessary to customize the major components of ISO 19650 defined in modules 2 through 5 and the open BIM standard defined in module 1 to fit the domestic situation and present it as a national standard. From a regulatory perspective, a strategy is required to gradually expand the mandatory application of ISO 19650 to some public construction companies, and a verification process through pilot projects should be accompanied before making it mandatory. Second, to directly utilize the ISO 19650 utilization model in work, it is necessary to incorporate the workflow of module 3 and develop a BIM project workflow support platform that includes the functions of module 6. For this purpose, it is important to convert document-level specifications into digital specifications and combine clauses and workflow units. In addition, a plan to link ISO 19650's key functions and data with commercial BIM platforms and enterprise ERP systems to operate needs to be prepared to increase the effectiveness of ISO 19650 adoption. Third, with the acceleration of the digital transformation paradigm, proactive future responses are needed, such as revising the BIM roadmap to prepare for the next maturity phase, as well as research on the introduction and stabilization strategy for digital information management maturity Stage 2 and BIM maturity Level 2. Epilogue In the era of digital transformation, the adoption and utilization of ISO 19650 in the global market has become an essential strategy for securing global competitiveness. To proactively respond to these changes domestically, an approach to the adoption of ISO 19650 has been suggested. To implement the core functions that can reflect the main components of ISO 19650 and be applied to practical situations, an ISO 19650 utilization model has been defined, and adoption plans and challenges for implementation in Korea have been proposed. It is anticipated that an adoption plan based on ISO 19650 will be reviewed in devising a national-level BIM information management operation system in the future. Department of Future&Smart Construction Research Date2023-02-27 Hit 3012
• Development of Environmental Simulator and Advanced Construction Technologies Over TRL6 in Extreme Conditions Development of Environmental Simulator and Advanced Construction Technologies Over TRL6 in Extreme Conditions ▲ Senior Research Fellow Shin Hyu-soung, KICT Department of Future & Smart Construction Research Prologue In May 2021, South Korea became the 10th participating country in the “Artemis Accords” project, an international piloted lunar exploration program led by NASA. As not only the United States but also Europe, China, Japan, and India have announced plans for lunar exploration, it feels as if the dream of space construction which had previously seemed far away has become more concrete. Furthermore, looking at Korea, which has not yet performed a concrete mission, it even makes one feel impatient to think that Korea may be falling behind again. The recent discovery of large amounts of ice in the lunar poles has accelerated the competition for lunar exploration. This is because ice not only can provide fuel for rockets, but also water and oxygen, which are essential for sustaining the life of astronauts. The Moon's gravity is only one-sixth that of the Earth's, which makes it easier to get out of the gravisphere with relatively little fuel. Therefore, the usability of the Moon as a stopover is growing, because space missions can depart from the Earth, recharge their fuel on the Moon, and take off to deep space such as Mars. In addition, since the Moon is close to the Earth, it is a good place to prepare various technologies to be used in outer space and on other planets and to conduct various scientific studies, which further increases the value of the Moon. All of these are reasons why many countries around the world intend to build bases on the Moon and use them for the long-term residence of astronauts. The Korea Institute of Civil Engineering and Building Technology (KICT) has been leading space construction research through the BIG Project since 2016. To strategically respond to the shifts in the space development paradigm caused by the efforts of nations around the globe, such as space base construction and resource development, this study aims to secure core construction technologies applicable in outer space, an ultra-extreme environment. Centering on the achievements of the 6th year (2021) of the study, this article aims to introduce the development status of the four core technologies in detail: Development of a full-scale chamber for realizing extraterrestrial planetary ground environment and verification technology; infrastructure construction technology using extraterrestrial planetary local materials; technology for the informatization of space for construction on the ground of planets; as well as the development of planetary ground investigation equipment and the planetary underground informatization technology. Development of a Full-scale Chamber for Realizing Extraterrestrial Planetary Ground Environment and Verification Technology The Full-Scale Dusty Thermal Vacuum Chamber (DTVC), which simulates the extreme lunar surface environment, is used to minimize the risk of failure in the real extraterrestrial space environment by verifying various technologies and equipment developed for lunar exploration. This DTVC was made in 2017 and installed in the Future Convergence Building, and was completed in 2019 after undergoing a stabilization test. The internal scale of DTVC is 50 m3, and it can simulate the temperature (from -190 ℃ to +150 ℃) and vacuum conditions (exclusive of ground: 10-6 mbar, Inclusive of ground: 10-4 mbar) of the lunar surface, and a large amount of lunar simulant is put inside the chamber to evaluate the impact of cosmic dust, etc. (Figure 1). The Dirty Thermal Vacuum Chamber (DTVC)'s performance in creating vacuum and temperature environments has been secured through studies in Years 1 through 5. In 2021, studies were carried out for the simulation of ground temperature conditions according to the lunar night and day conditions; the measurement of the thermal conductivity of the ground according to the vacuum pressure for creating the ice ground; as well as the system for simulating electrostatic charging environments on the lunar surface and its measurement study. As the lunar surface has no atmosphere, the temperature near the equator can rise to 120 °C during the day, and can drop to as low as -170 °C at night. However, since the thermal conductivity of lunar simulant is low in a vacuum environment, the temperature change of the soils below 10 cm depth is not large, and the low-temperature state is maintained. The soil temperature according to the depth in the low/high-temperature environment of the full-scale chamber constructed based on these data was measured (Figure 2), and further study will continue to improve the performance of simulating the temperature of the ground. In addition to temperature and vacuum, the characteristic lunar surface environmental condition is electrical, which shows a positive potential of less than +20 V under the influence of sunlight during the day and a negative potential of hundreds to thousands of V at night under the influence of the Earth's plasma (Figure 3). This characteristic is considered a threat to the long-term residence of astronauts and equipment on the lunar surface, and it is necessary to understand and develop technologies to address it. To simulate this electrical environment in the DTVC, an electrification environment simulation system using ultraviolet lamps and electron beams was built in a small vacuum chamber (Figure 4). Based on the electrical data on the lunar surface, a similar environmental simulation and a measurement method were devised. In this year, the 7th year of the DTVC, a study on ground cooling in the chamber and measuring the potential of the ground charged with static electricity in an electrical electrification environment will be conducted. Through such research, it is planned to advance the environmental simulation performance of the DTVC and develop it into a more reliable construction technology verification facility in the lunar environment. Infrastructure Construction Technology Using Local Materials of Extraterrestrial Planets To build a lunar base, construction materials are needed. As the cost of transporting such materials from the Earth to the Moon would be astronomical, it is essential to develop a technology that makes it possible to produce construction materials using resources available on the Moon. To this end, a study is being conducted to solidify the “Lunar Simulant,” local resources available on the Moon, through a microwave-sintering technology in order to use it as a construction material. Sintered pellets of lunar simulants are created when lunar simulants (KLS-1) are densified at a temperature of 1,080°C or higher through the microwave-sintering method, and the density and compressive strength of the sintered pellets are increased as the sintering temperature rises. The thermal expansion characteristics of all materials used in the construction of the lunar base are very important, as repeated contraction and expansion of construction materials with the extreme temperature change of the Moon can cause cracks in the structure. The thermal expansion coefficient of the sintered pellets of lunar simulants produced by the microwave sintering method is about 5 × 10-6 °C-1 within a range similar to the lunar surface temperature (from -100 to 200 ℃), which was confirmed as similar to the thermal expansion coefficient of actual lunar rock. In addition, the thermal expansion coefficient of the sintered pellets of the lunar simulants did not change significantly even after processes of heating-cooling-reheating, so it was confirmed that the microwave sintered lunar simulant had high thermal resistance even under the Moon’s extreme temperature changes. To use the sintered pellets of the lunar simulant as a lunar base construction material, it is first necessary to review their homogeneity. In this study, the porosity was estimated through the Statistical Phase Fraction (SPF) method using X-ray CT images of the sintered pellets of lunar simulant to evaluate the homogeneity based on the porosity distribution of the material. It was confirmed that the total porosity of the sintered pellet of lunar simulants, estimated by the SPF method, was almost identical to the porosity calculated through the density analysis. Through estimating the local porosity of the local sintered pellets by dividing the CT image of the sintered pellet of lunar simulants into unit cells with a constant volume, it was confirmed that the mean porosity of 1,080 ℃ and 1,100 ℃ sintered pellets of lunar simulants were 30.4±2.1% and 27.1±2.9%, respectively, and distributed in the ranges of 26-40% and 20-36%. At the same height, the porosity decreases from the outside to the inside of the sintered pellet. This is due to the characteristics of microwave heating, which has a higher internal than external temperature, and it can be seen that a denser structure is formed in the center of the sample. Currently, sintered block of lunar simulant is being manufactured to increase its utility as a construction material, and to apply microwave sintering technology to a real lunar high vacuum environment, microwave vacuum sintering equipment must be built and sintering experiments conducted. Technology for Informatization of Space for Construction on Extraterrestrial Planetary Ground To select the optimal site for the construction of a lunar base, a lunar topography survey is essential. However, Global Positioning System (GPS) is not available on the Moon, and the Moon's Permanently Shadowed Regions are low illuminance areas without sunlight. Therefore, this study aimed to develop a real-time 3D topographical information technology based on an unpiloted vehicle that can be used to construct the high-precision 3D topographic map required for design and construction on the lunar surface. In this study, research was conducted to acquire real-time three-dimensional topographic information in low illuminance and GPU-shadowed environments by using a sensor combination of a stereo camera mounted on an unpiloted vehicle and an Inertial Measurement Unit (IMU). In particular, a self-supervised CNN-based image enhancement module was developed to maximize mapping performance in a low illuminance environment, and mapping performance with a mean error of less than 7 cm in a low illuminance environment was secured. In addition, simulated planetary topography consisting of craters, rocks, hills, soil, and gravel areas was built in the KICT's indoor simulated terrain laboratory and SOC Demonstration Research Center (Yeoncheon), and verification experiments for unpiloted topography informatization technology were conducted (Figure 7). The research was conducted on object recognition in images of constructed simulated extraterrestrial planetary topography, region classification, and evaluation of the similarity of the same object in other images. To automatically classify objects and regions of interest in the targeted areas, a topography and terrain feature recognition and region classification technique using Mask R-CNN, an open source program for deep learning region recognition, was developed. We also developed a matching method for identical object and terrain features in multiple images using the Triplet network, and completed a major object matching system between aerial photos and unpiloted-vehicle topographic images (Figure 8). In the future, we plan to develop a GIS-based unpiloted topography informatization technology system by improving the accuracy of unpiloted topography informatization technology and artificial intelligence object matching technique and combining them. Development of Extraterrestrial Planetary Ground Investigation Equipment and Extraterrestrial Planetary Underground Informatization Technology Moon explorations, which had stalled for a while after humans first successfully landed on the Moon, began to become active again when the existence of ice at the lunar poles was confirmed. To analyze the ice and underground resources that exist at the poles of the Moon, drilling equipment must be mounted on the exploratory Lander or Rover. In order to transport and operate such equipment on the Moon, an extreme environment, requirements in terms of small size, light weight, low power, high efficiency, and high performance have to be met. In this study, a prototype of drilling equipment that can be operated in atmospheric pressure and low temperature environments was first developed. In addition, in consideration of transportation needs, miniaturization of 0.27 m3 grade, weight reduction of 18.5 kg grade, and low power consumption of 44.4 W grade were secured. Prototype drilling equipment was pre-verified using artificial ice in the freezing chamber, and a field study was conducted under low power, low reaction, and waterless conditions for sea ice and frozen soils around the Jang Bogo Research Station to evaluate the drilling performance and identify problems in drilling the sea ice frost heaving (Figure 9). By performing the drilling performance and reliability evaluation under various local conditions, it was confirmed that drilling failure due to slip of the bit-cutting area occurred when the vertical reaction was 25 N or less, and drilling failure due to jamming occurred when it was 125 N or more. In the range of vertical reaction of 50 to 100 N and rotation speed of 25 to 125 rpm, drilling reliability of at least 60% was secured. Epilogue The construction of a space base, a dream of humankind for decades, is gradually becoming a reality. Space powers are competing with each other in terms of piloted lunar exploration and base construction plans to be the first to occupy the Moon. Relatively speaking, Korea's space development research lags far behind. However, the space construction field, which has just taken its first steps, has relatively low entry barriers compared to other space fields. As such, simply by securing the core technologies Korea can enter the ranks of advanced countries at the forefront of space exploration. It is hoped that the space construction technology developed by the KICT will be the first core technology to open the gates, and that it will leap forward as an institution leading the global space construction field in the future. Department of Future&Smart Construction Research Date2022-09-27 Hit 2356
• Trends in Extraterrestrial Planetary Resource Exploration and International Technology Trends in Extraterrestrial Planetary Resource Exploration and International Technology ▲ Research Specialist Ryu Byung-hyun, Department of Future & Smart Construction Research Prologue Since the Space Age began in the 1960s, there have been 60 lunar missions, eight of which have been human piloted or crewed missions. Apollo 11 was the first crewed mission to land on the moon in 1969, and later Apollo 15 brought rock samples back from the surface, putting more weight on the hypothesis that the moon was born from a massive collision with Earth. Human understanding of the extraterrestrial universe has been broadened through such lunar exploration, with human interest in Mars growing even further since. In 1997, Mars Pathfinder was the first mission to land a mobile rover on the surface of Mars, with photos sent from the rover attracting great public attention and further promoting the Mars exploration. However, as the Mars exploration became more challenging, humankind has begun to show interest in lunar exploration once again. The reason that governments and enterprises of each nation are actively engaged in lunar exploration in the space development competition is that the moon is not a mere subject of mystery but is closely related with the future of humankind. Our terrestrial resources are naturally and gradually being depleted due to their natural scarcity, even though the rate may vary depending on how quickly they are consumed. The anticipation that humankind could continue to subsist solely on Earth's resources indefinitely is now long gone. Instead, humankind has been devising ways to conserve resources while producing various solutions such as the development of alternative energy and alternative materials. On the other hand, humanity is also pondering how to make use of extraterrestrial resources. Many of the objects in space are astronomically distant from us, with only a few countries having the means of transportation, and the costs are likewise astronomical. Tapping into extraterrestrial resources was thus merely a topic of the imagination in the past. Recently, however, new ideas have been emerging to tap into the virtually unlimited resources of outer space. Discovery of Extraterrestrial Planetary Resources The discovery of extraterrestrial planetary resources dates back to when the samples that the Apollo spacecraft brought back from the moon were analyzed. Since that moment, when helium-3 was discovered in these rocks brought back to Earth, the amount of helium-3 in the lunar rocks was investigated, and the resources from each lunar landing site began to be estimated. The US had collected 382 kg of return samples from the lunar sea and high mountain regions of the moon through its Apollo program (Lucey et al., 2006), and Russia also analyzed 170.1 g of lunar samples (Wikipedia, 2017b) brought back to Earth, enabling them to understand the soil and rocks on the lunar surface. This was done to estimate where and how much of the resources were distributed on the lunar surface based on the lunar samples. Later, there was a new discovery from the Clementine mission of iron and titanium, forming the mineral map for the moon. The discovery of permafrost also suggested, for the first time, the possible presence of water on the moon. Launched in 1998, the Luna Prospector mission mapped the water as well as thorium and potassium, the natural radioactive elements, using epithermal neutrons. Most of the findings from these past missions were obtained by remote sensing, and they played a major role in creating maps that provide an understanding of the resources on the lunar surface. The gamma-ray spectrometers used in the Luna Prospector and Kaguya missions enabled the provision of maps of several major elements, and the gamma-ray spectrometer of the Kaguya mission produced, for the first time, the map for uranium. After remote sensing by the orbiter, Chang'e-3 was able to perform elemental analysis on the lunar surface using an x-ray spectrometer, but continued analysis was not possible due to the difficulty of rover survival. This phase of human discovery of resources on the moon and the estimation of their distribution is evolving into an on-site extraction experiment to confirm and utilize the findings on the lunar surface in the future. We are turning into reality our dreams of starting resource exploitation activities that would allow us to settle on survivable area on the moon, utilize local lunar resources, and provide energy resources, such as helium-3, which will be needed on Earth in about 10 years. Resources in Lunar Poles The Lunar Crater Observation and Sensing Satellite (US LCROSS) and Lunar Reconnaissance Orbiter (LRO) missions to the Moon were launched in June 2009. Following the new findings obtained as a result of the LCROSS collision with Cabeus, a crater at the moon's southern pole, the possibility newly presented itself for humanity's future utilization of the resources of the lunar poles. LCROSS announced that it had discovered water in the lunar southern pole, which is a requirement for humankind to build a lunar base. LRO has laid the foundation for the construction of a lunar base by orbiting the moon at an altitude of 50 km, focusing on the search for resources, including water, and scanning the lunar radiation environment. Based on the findings obtained using the Diviner Spectrometer mounted on the LRO, volatile substances and rare metals as well as water, brought by comets and asteroids, were estimated to have been deposited in permafrost on the lunar surface for billions of years since the moon was formed, and these materials were found to have been eroded at a loss rate of 1 mm per billion years (Paige et al., 2010), signifying the importance of the utilization of lunar permafrost resources (Colaprete et al., 2010; Gladston et al., 2010). These new findings provided an opportunity for major countries including the United States to seriously consider their plans for the construction of lunar bases as intermediate bases and the utilization of lunar resources in planning for the long-term exploration by humans of planets in more distant solar systems, as well as Mars. As a result of the LRO/LCROSS lunar missions, it was discovered that the lunar southern pole, a permafrost region, contains several volatile substances and has high content of rare metals such as gold, silver, and mercury. These substances are believed to have been brought from outside the moon (Gladstone et al., 2010). Drilling for Extraterrestrial Planetary Resources Recently, it has been revealed that water is present in the form of ice on extraterrestrial bodies such as the moon, and extraterrestrial resource exploration projects are being actively conducted centering on National Aeronautics and Space Administration (NASA) and China Association for Science and Technology (CAST). Furthermore, the European Space Agency (ESA) has announced the construction of the Moon Village and is leading the international cooperation necessary for the development of core technologies. In 2018, a team of researchers at the University of Hawaii discovered ice traces in the “Permanently Shadowed Regions” at the northern and southern poles of the moon. It has been speculated for a long time that water or ice has existed on the moon, but the research team at the University of Hawaii was the first to present conclusive evidence of this. The existence of water components has been identified in the past, but this was the first time that the molecular (H2O) form of water has been discovered. As the existence of usable water became clear during the lunar missions, the lunar explorations prepared by different countries were also able to gain momentum. Water in ice or liquid state cannot exist on the surface of the moon because its temperature rises to 130°C or higher when exposed to sunlight, which causes it to evaporate. Permanently shadowed regions in the surface craters of the south pole of the moon, which never receive direct sunlight, were thought to have ice because the temperature is believed to always be kept below -180°C. Based on this, scientists have speculated that water may exist in these regions. In 2009, NASA confirmed the presence of water by deliberately crashing the LCROSS spacecraft on the southern pole of the moon. However, it was not clear whether this was an actual form of water or of other substances (Li S, Lucey PG, Milliken RE et al., 2018). Water can be detected using infrared light, but so far only the 3㎛ wavelength band has been employed. With this wavelength, the water molecule could not be clearly distinguished from the hydroxyl group (-OH) bound to the mineral. If hydrogen and oxygen are present but merely as components in the state of attachment to a mineral rather than in the form of water, other additional processing is required to make use of it from this state. So far, it has been speculated that water in the form of water molecules is present in the regions of the lunar southern pole at a concentration of about 100 - 400 ppm. Whether water exists there depends on the surrounding topography, and it has been analyzed that this does not mean that water is present throughout the region. As this type of water exists in vitrified soil or between gravels, it is assumed that it remains even in the extreme environment of the moon. In addition, it was analyzed that there are many places where water can remain in the form of ice at the northern and southern poles. A team of scientists led by Professor Paul Hayne of the Laboratory for Atmospheric and Space Physics at the University of Colorado Boulder, USA said in a study published in the Nature Astronomy journal on the same day that “Permanently Shadowed Regions,” which may serve as ice reservoirs or cold trap at the moon’s northern and southern poles, measure at approximately 40,000 m2, or about six times the size of a football field (P. Hayne, O. Aharonson, N. Schörghofer, 2020). As the presence of water at the southern pole became evident, nations are pushing ahead to explore this area competitively. On the moon, the southern pole has more uneven surfaces compared to those of the equator, and communication is difficult, making exploration even more challenging. However, because of the presence of water, which is essential for life and can be used for exploration, different countries have designated the southern lunar pole as an exploration target. The United States announced the "Artemis Program" to send crewed spacecraft to the moon by 2024 and designated the southern pole as a landing point. China is also planning a "Chang'-e 7" mission to the Moon in 2024. NASA and CAST are scheduled to bring soil samples from extraterrestrial planets back to Earth through their extraterrestrial resource exploration projects. To accomplish this, it is necessary to develop drilling equipment that can be stably operated in extreme space environments. An extraterrestrial drill is equipment that can collect not only extraterrestrial planetary surface samples but also underground samples during the drilling process. It should thus be developed in an ultra-lightweight and compact form that enables uncrewed operation with low power and high efficiency in the extreme environment of space as well as transportation from Earth to the extraterrestrial bodies. The Korea Institute of Civil Engineering and Building Technology (KICT) is developing a drill for extraterrestrial resource exploration by reflecting this trend of international space missions, and intends to devise a method for evaluation that can directly estimate the uniaxial compressive strength of the ground that resists destruction by using the drilling information of the drilling equipment. In this regard, we would like to introduce here a series of processes of performance verification for a drilling rig, the development of bits, and the derivation of evaluation techniques, all through laboratory experimentation as basic research. Trends in Extraterrestrial Planetary Geotechnical Survey Technologies NASA succeeded in drilling into the lunar surface for the first time after a crewed lunar landing with the Apollo 15 mission in 1971. The drilling equipment used was called the Apollo Lunar Surface Drill (ALSD) and was a battery-powered portable drill used for drilling by the Apollo 15 crew. NASA's drilling equipment has been advanced dramatically through research and development, and its representative drilling equipment is the icebreaker drill developed by Honeybee Robotics, Ltd. This icebreaker drill was developed to search for signs of life in the ice-rich regions of Mars. Considering the conditions, such as lower gravity and atmospheric pressure compared to those of Earth, finite electrical energy, and an environment where it is difficult to use cutting oil, this equipment set its performance target of achieving 1 m deep drilling depth within one hour at power of 100 W and thrust of 100 N or less. It succeeded in achieving this performance target in a vacuum chamber experiment with an environment similar to that of Mars. The icebreaker drill consists of a deployment boom, Z-stage, rotary-percussive drill head, auger, drill bit, and sampling system. The deployment arm is a 3-DOF (Three Degrees of Freedom) cantilever that moves the Z-stage to the drilling point. The Z-stage is composed of a rail that moves in a straight line, a table and pulleys that move in a straight line, and cables, and is a device that transports an auger and drill bit in a straight line in the drilling direction. The rotary-percussive drill driving unit applies a rotary percussion to the auger drill bit to provide the torque and thrust required for drilling. The auger implements cutting material transport and sample collection functions. The drill bit is a cutter placed at the end of the auger. The icebreaker drill bit has an embedded temperature sensor and electrical conductivity sensor to monitor the temperature of the drilling environment around the drill bit and the physical condition of the materials being cut in real time during drilling. The sample extraction unit consists of a device for extracting the cut materials attached to the auger groove. After the Chang'e 3 mission's successful landing on the moon for the third time in the world in 2013, CAST launched the Chang'e 5-T1 (嫦娥五号T1), an uncrewed spacecraft that would bring samples from the moon back to Earth in 2020. Chang'e 5-T1 was launched from the Wenchang Space Launch Center in Hainan province, China, and landed on the "Ocean of Storms," a plain in the northwestern region of the moon, and collected about 2 kg of soil and rock samples. It then launched again from the lunar surface and made its way back to Earth. In December 2020, the Chinese National Space Agency (CNSA) announced that a capsule containing soil collected from the moon by Chang'e 5-T1 had landed in Inner Mongolia. China thus became the third nation to have brought lunar soil to Earth, after the United States and the Soviet Union. Chang'e 5-T1 has succeeded in bringing lunar soil back to Earth for the first time in 44 years since the Soviet Union's uncrewed Luna 24 mission in 1976. For lunar drilling research, China has been manufacturing a drilling test bed since 2016 based on Zhang Tao et al. (2016) and conducted experiments on drilling equipment to be applied in its mission to explore lunar resources. Mechanically, it is composed mainly of a body frame, a rotary mechanism, a penetrating mechanism, and encoders. The body frame is a structure that supports the rotary mechanism and the penetrating mechanism. The rotary mechanism is the unit that causes a drill tool to rotate, and the penetrating mechanism is the unit that forces the drill tool to drill into the lunar surface and sample the lunar soil. The encoder unit is composed of two encoders, which have the respective functions of measuring the rotational velocity of the rotary mechanism and the drilling speed of the penetrating mechanism. Status of KICT’s Development of Extraterrestrial Planetary Drilling Equipment The world's space powers are competing fiercely for resource exploration by developing drilling equipment that can be used in the environments of space. Accordingly, the Korea Institute of Civil Engineering and Building Technology (KICT), as a latecomer, began developing drilling equipment for the exploration of extraterrestrial resources in 2016. Currently, we are manufacturing prototypes of drilling equipment and conducting performance tests under various extreme environmental conditions. Mechanically, this equipment is composed mainly of a body frame, a driving unit, and a rotary unit. The body frame maintains the vertical position of the drill and supports the driving unit and the rotary unit. The driving unit consists of a vertical motor capable of vertical transport and a gearbox for speed control. The rotary unit consists of a bit-auger rotary motor, a bit-auger connection part, and a gearbox for rotation speed control. The auger serves to transport the cut materials and collect samples. The bit refers to a cutter installed at the end of the auger. One of the characteristics of this drilling equipment is that it has an integrated load cell, which can simultaneously measure reaction force and torque. In addition, the drilling speed can be estimated by the number of revolutions of the vertical motor, as it moves along the timing belt attached to maintain the vertical feed and vertical position of the drilling equipment. An encoder-coupled planetary reduction DC induction motor is mounted, capable of precise control of 48.6 W output for rotation of the auger, with a gear reduction ratio of 1:230. The output shaft of the motor is connected in the order of a reduction gear, a drive shaft, a torque meter, a drill bit clamp, and a drill bit. The driving force of the motor is used to cut and crush the center of the specimen by rotating the drill bit at the bottom. The motor is driven with a power of 24 V, rated current of 2,850 A (ampere), and rated speed of 3,550 rpm, and drills while rotating at a constant speed under the rated load. Department of Future&Smart Construction Research Date2022-03-28 Hit 1594

첫페이지이전페이지1다음페이지마지막페이지