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• Carbon-Eating Concrete: A Means of Reaching Carbon Neutrality Research Fellow Park Jung-jun, Department of Structural Engineering Research (Carbon-Neutral Construction Materials Team), KICT Each year, the Earth sends increasingly urgent warnings in the form of extreme weather events. Recurrent damage to lives and infrastructure caused by heavy rainfall, droughts, and typhoons has made greenhouse gas reduction no longer something to aspire to, but essential for survival. The cement and concrete industry is a particularly large emitter, accounting for approximately 8% of global greenhouse gas emissions. To directly confront this reality, the Carbon-Neutral Construction Materials Team at the Department of Structural Engineering Research, KICT, has developed Carbon-Eating Concrete (CEC) technology. Their goal extends beyond simply reducing emissions: it is to accelerate carbon neutrality across the entire concrete industry. Developing CEC Technology for Carbon Neutrality The Carbon-Neutral Construction Materials Team is engaged in continuous research with the aim of achieving net-zero carbon emissions in the construction industry. In response to the urgent global demand for carbon reduction technologies, the team is tackling the greenhouse gas challenges of the cement and concrete sector head-on. At the core of this research lies Carbon-Eating Concrete (CEC) technology. The essence of CEC is to react the carbon dioxide generated during concrete’s production with components inside the concrete, permanently storing CO₂ in a stable mineral form while simultaneously enhancing the concrete’s strength and durability. This represents a circular approach in which exhaust gases are treated not as pollutants, but as valuable resources. The research team is exploring the potential for CO₂ storage across the entire lifecycle of construction materials—including cement, aggregates, and mixing water—while focusing on maximizing the carbon neutrality impact of the concrete industry as a whole. Utilizing the largest CO₂ curing facility in Korea, the team has successfully demonstrated direct CO₂ storage in precast bridge deck slabs. In addition, they have developed a CO₂ treatment technology for recycled ready-mixed concrete wastewater that can also be applied to cast-in-place concrete, achieving world-class efficiency. Ultimately, through a full lifecycle research framework encompassing material development, structural performance evaluation, field application, and policy recommendations, the team is working to ensure that the technology moves beyond the laboratory to become firmly established at real-world construction sites. CEC Technology: A Game Changer in Accelerating Carbon Neutrality The International Energy Agency (IEA) estimates that carbon capture and utilization (CCU) technologies that use CO₂ during concrete production have the potential to contribute 1–15% of the total 10 Gt CO₂ reduction target under the broader CCUS (Carbon Capture, Utilization, and Storage) framework. This assessment highlights CEC technology as a practical and globally applicable solution for accelerating carbon neutrality efforts. Accordingly, CEC is often described as a key player within the CCUS technology chain. This is because CEC is one of the very few technologies capable of utilizing the large volumes of CO₂ captured from industrial sites while simultaneously ensuring its safe and permanent storage. Unlike other CCU approaches that remain largely at the proof-of-concept stage, such as carbon-to-fuel or carbon-to-oil conversion, CEC has already demonstrated its feasibility for on-site application through pilot and demonstration projects. The research team estimates that converting just 20% of domestic concrete production to CEC technology could reduce CO₂ emissions by approximately 520,000 tons per year, equivalent to about 4.9% of Korea’s national CCUS reduction target. In this sense, CEC technology represents a critical lever for transforming the construction industry into an active contributor to climate change mitigation. Convergent Research Addressing Key Barriers A major strength of the River Experiment Center lies in its laboratory-to-field integrated approach to developing CEC technology. Rather than focusing solely on injecting CO₂ into concrete, this work represents a convergent research effort that connects the entire chain of carbon capture, utilization, and evaluation into a single, integrated framework. To this end, experts from industry, academia, and research—including the Department of Structural Engineering Research as the core hub, together with the Department of Building Research, the Department of Fire Safety Research, Shinhan University, Yonsei University, and Jiseung C&I Co., Ltd.—have participated from the early stages through regular seminars and discussions. Through the process of understanding different disciplinary perspectives and sharing data, the research team has been building a model for effective convergent research. The greatest challenge was the scale-up process, in which technologies that had proven successful at the laboratory level had to be expanded to an industrial scale. This involved more than simply increasing reactor capacity; it required verification that prototypes could be mass-produced while meeting the quality and reliability standards demanded in real-world applications. When unexpected reaction variability emerged at larger scales, the team designed and fabricated multi-stage mock-up reactors and repeatedly analyzed simulations and experimental data to identify optimal operating conditions. As a result of these efforts, large-scale demonstration experiments are now actively underway at ready-mixed concrete and precast product manufacturing plants. A virtuous cycle has also been established, in which data obtained from field demonstrations are fed back into research to further refine equipment and processes. Through a process of continuous adjustments and optimizations, CEC technology is steadily evolving into a field-ready standard. Lifecycle Research Capability and Teamwork The greatest strength of the Carbon Neutral Construction Materials Team lies in its end-to-end research capability, spanning the entire continuum from materials development to institutional and policy improvement. The team’s work goes beyond materials innovation to encompass structural safety evaluation, field application validation, technology standardization, and policy recommendations, all conducted within a coherent and integrated research framework. This comprehensive approach enables potential challenges arising at construction sites to be anticipated in advance and addressed effectively, significantly enhancing the practicality and scalability of the technology. All of these achievements are rooted in the strong trust and collaboration among team members. The Carbon-Neutral Construction Materials Team fosters a horizontal culture in which everyone, regardless of their rank or years of experience, is encouraged to freely share ideas, respect diverse perspectives, and work together toward optimal solutions. Through a process of overcoming numerous trials and setbacks, team members have continually supported and motivated one another. This collaborative culture has provided researchers with sustained positive momentum, and become a driving force in their collective efforts to build a sustainable future. Department of Structural Engineering Research Date2025-12-22 Hit 17
• Core Solution for the Era of Fully Autonomous Driving: Physical Infrastructure Supporting Autonomy Senior Researcher Kim Young-min, Department of Highway and Transportation Research, KICT Prologue To operate independently, autonomous vehicles (“AVs” hereinafter) must be capable of perceiving and interpreting their surroundings. In essence, they need to perform the same sequence of actions that human drivers carry out—Perception–Identification–Emotion–Volition (PIEV). To achieve this, AVs must be equipped with systems and performance capabilities that support this sequence. For AVs, the functions that parallel the human PIEV process are recognizing the driving environment and controlling the vehicle based on that recognition. The environment they must process includes not only the fundamental road layout (e.g., horizontal and vertical alignment, lane configuration) but also dynamic, real-time information, such as the presence and movement of other road users (vehicles, pedestrians, etc.) and the traffic regulations governing road use. Up to now, road infrastructure systems have been developed and operated with human drivers as the primary consideration. To achieve the commercialization of fully autonomous driving technology, it is essential to re-examine road infrastructure systems with AVs as the primary consideration. The Korea Institute of Civil Engineering and Building Technology (KICT) has pursued various R&D initiatives around strengthening the role of road infrastructure in the age of autonomous driving (for related content, see the Spring 2025 special feature “Future Road Development for Cooperative Autonomous Driving”). This article introduces the Physical Infrastructure Supporting Autonomous Driving currently being developed at the KICT. Background and Purpose of Technology Development To realize fully autonomous driving—defined as Level 3 or higher under the SAE (Society of Automotive Engineers) standards, where control authority shifts from the human driver to the vehicle—it is essential to combine advanced AI-based environmental perception using onboard sensors with technologies that link static and dynamic information from high-definition road maps, known as the Local Dynamic Map (LDM). Together, these technologies enable vehicles to perceive their surroundings with high-precision positioning. This approach represents the core concept of cooperative autonomous driving, in which infrastructure supports autonomous vehicles in carrying out driving tasks. To make this vision a reality, various forms and methods of infrastructure support have been proposed (see Figure 1). Let us return to the perspective of human driving behavior. The information a driver uses to perform driving is more extensive than commonly assumed, and the cognitive processes involved in decision-making are highly complex. For example, the act of lane changing stems from several decision-making factors and resolutions. This includes decisions such as recognizing that the current lane is more congested than an adjacent lane and deciding to change lanes, determining that the lane ahead is blocked due to construction or other reasons and therefore a forced lane change is unavoidable, or choosing to move into a lane closer to the intended direction in order to make a left or right turn at an intersection. At a deeper level, driving involves collecting “evidence” for each decision, followed by “reasoning” to reach the final judgment. In short, the decision-making process required for driving combines sensory inputs—such as visual recognition of an obstacle’s shape or auditory recognition of a horn—with prior driving experience and accumulated know-how in situational judgment. AVs must carry out the same processes as human drivers. At this point, the role of road facilities—referred to in this article as “physical infrastructure supporting autonomous driving”—is revealed. Because sensor-based perception systems have inherent limitations, AVs are currently required to transfer control authority back to the driver in what are commonly called “handicap situations and zones” (Jeon and Kim, 2021). Representative examples include reduced visibility due to weather conditions such as fog, which makes it difficult for vision sensors to collect information, and lane closures caused by roadwork; these are classified as typical “autonomous driving handicap situations and zones.” If “physical infrastructure supporting autonomous driving” can provide support in such “handicap situations and zones” by contributing to the “decision-making process,” more specifically to the “evidence collection process for decision-making” and to “reasoning using decision evidence,” it can offer practical and meaningful assistance for AV operations. The implementation of “physical infrastructure supporting autonomous driving” can be broadly categorized into two types. The first involves enhancing existing road facilities so that they are more easily detected by AV sensors. In practice, this means improving the sensor-based perception performance of road facilities—while preserving their inherent functions and properties—by accounting for the characteristics of key vehicle sensors used for environmental perception (e.g., cameras, LiDAR). Examples include adjusting the color or material of facilities within existing regulatory limits, or making structural modifications that expand sensor-detectable areas without altering their outward appearance. The second type involves leveraging the physical properties of road facilities to provide the AV with information that can serve as more reliable evidence in its reasoning process. For instance, facilities shaped like conventional traffic signs can display encoded information that AV sensors can detect and interpret, thereby delivering critical road operation data for vehicle control. This approach can be viewed as equipping AVs with functions equivalent to those that road facilities provide for human drivers—such as traffic regulation signs that indicate required actions, or guide signs and safety facilities that offer useful reference information while driving. Development and Verification of Physical Infrastructure Supporting Autonomous Driving: Focus on Lane-Closure Sections In 2024, the research team developed a prototype AV (see Figure 2). Although it is just one of many AVs designed and manufactured in South Korea, this vehicle has a unique function: it enables “vehicle control and autonomous driving through physical infrastructure support.” By incorporating physical infrastructure into vehicle control, the research team compared AV perception performance in “handicap situations and zones,” as well as vehicle behavior with and without infrastructure in those conditions. This made it possible to verify the suitability of the physical infrastructure system for autonomous driving. Every day, countless events unfold on the road. Among them, one of the most critical situations directly affecting vehicle operation is the lane closure. Lane closures often occur due to road maintenance or accident response, and vehicles must detour around the closed lane in order to continue driving. Human drivers recognize and interpret lane closures through multiple cues—for example, visually confirming traffic control devices such as cones or guard barriers, observing hand signals from traffic controllers like police officers or flaggers, or noticing forced merges of preceding vehicles. AVs, however, have clear limitations in carrying out such reasoning and judgment processes. Given that lane closures are highly dynamic and variable on roadways, it is expected that map-based electronic information systems alone will not be sufficient to provide reliable information in these situations. The research team devised a system that enables AVs to more easily recognize lane-closure situations by utilizing “encoded signs” that can be detected through road facilities (see Figure 3). In lane-closure sections, the vehicle must perform lateral control, which consists of two tasks: avoidance control, where the vehicle detours around the closed lane, and return control, where the vehicle decides whether to return to its original lane after passing the closed section, depending on the requirements of the global driving path. To achieve this, the team applied a technology that recognizes point cloud data (PCD) patterns obtained through in-vehicle LiDAR, enabling AVs to detect lane-closure situations via road facilities and incorporate this information into vehicle control. This approach takes advantage of LiDAR’s greater robustness under adverse weather conditions (e.g., heavy rain, fog) compared to vision sensors, thus addressing the “visibility obstruction” handicap situation that cannot be easily resolved by simply improving conventional vision sensor–based perception (Kim et al., 2024). The following are the speed and angular velocity values measured inside the AV when passing through a lane-closure section, both without and with the installation of physical infrastructure supporting autonomous driving. This experiment was conducted by recreating a lane-closure environment at the Yeoncheon SOC Demonstration Center and observing how the vehicle’s behavior differed depending on the presence or absence of physical infrastructure. When the AV recognizes the lane-closure section, lane-change control is performed, during which the vehicle reduces speed to an appropriate level and executes a turning maneuver to change lanes. In this process, if physical infrastructure that provides lane-closure guidance exists, the AV can recognize the lane-closure section in advance. Compared to abrupt maneuvers such as sudden deceleration or sharp turns—often carried out by AVs when facing unexpected physical situations that make normal driving difficult—this advance recognition induces smoother driving. Numerically (see Figure 4), without the use of lane-closure guidance infrastructure, the AV reduced its speed by up to 20 km/h when changing lanes, with angular velocity reaching a maximum of 0.15 rad/s. In contrast, when physical infrastructure was utilized, the AV reduced its speed by only up to 10 km/h to pass through the section, and its maximum angular velocity remained within 0.10 rad/s, confirming quantitatively that more stable driving was achieved. The results of this experiment indicate that the physical infrastructure supporting autonomous driving not only has positive effects on AVs themselves but can also generate even greater benefits in situations where AVs and conventional vehicles coexist. From a traffic flow perspective, large fluctuations in the speed and angular velocity of an individual vehicle make the vehicle a so-called “troublemaker” that disrupts overall traffic stability. The function of physical infrastructure that ensures AVs are controlled so that they do not behave in ways that appear unusual compared to human drivers contributes to improving the stability of mixed traffic flow involving both AVs and conventional vehicles, and is therefore expected to positively influence the broader adoption of AVs. Epilogue As of 2025, many experts believe that autonomous driving technology is currently in a stagnation phase of development and diffusion known as the “Chasm.” In the early 2010s, when Google first unveiled its autonomous vehicle to the public, most countries had set targets for the commercialization of autonomous driving that were earlier than 2020. Today, in the mid-2020s, only a very limited number of production vehicles equipped with autonomous driving functions at SAE Level 3 or higher—a recognized milestone for commercialization—actually exist, and even these are constrained to operating only within various limitations defined by their Operation Design Domain (ODD). This reality implies that there are significant technological challenges that must be solved before we reach the era of fully autonomous vehicles, and at the same time highlights the need for new methodologies and approaches. Various R&D cases conducted thus far demonstrate that cooperation between vehicles and infrastructure is indispensable for the commercialization of fully autonomous driving. The methodology introduced by the research team in this article—namely, constructing an environment in which AVs can more actively utilize road facilities during the driving process and applying this approach to alleviate the difficulties of AV decision-making and control in “handicap situations and zones”—is expected to serve as a core solution that can accelerate the advent of the fully autonomous driving era. References Kim, Young-min; Park, Beom-jin; Kim, Ji-soo (2024). A Study on the Development and Verification of Infrastructure Facilities Supporting AV Positioning Using Mobile LiDAR. Journal of The Korean Society of Intelligent Transport Systems, Vol. 23, No. 6, pp. 203–217. Jeon, Hyun-myung; Kim, Ji-soo (2021). Analysis of Handicap Situations and Their Causes in Autonomous Vehicles through IPA and FGI. Journal of The Korean Society of Intelligent Transport Systems, Vol. 20, No. 3, pp. 34–46. Korea Intelligent Transport Systems Consortium (2024). Stage Report on the Development of a Digital Road and Traffic Infrastructure Convergence Platform Based on Crowdsourcing. Department of Highway & Transportation Research Date2025-09-24 Hit 257
• The Current State of Cable Tension Monitoring Technology in Cable-Stayed Bridges Senior Researcher Park Young-soo, Department of Structural Engineering Research, KICT Prologue The Special Act on the Safety Control and Maintenance of Establishments defines criteria for managing facilities, including bridges, primarily based on their scale and type, and stipulates that special bridges must be monitored and managed through precise measurements. Among these special bridges, the cable-stayed bridge is a representative cable-supported structure, in which the deck is supported by stay cables connected to towers. Cable-stayed bridges offer improved structural efficiency by combining the tensile strength of cables with the bending and compressive strength of towers and decks. They are particularly suited for long spans, but because of their aesthetic appeal, are also increasingly being adopted for shorter spans, resulting in a steady increase in the number of cable-stayed bridges in service. In such cable-supported structures, the cables are critical structural components. Their tension force and damping ratio affect not only the behavior of the cables themselves but also the overall stability of the bridge. As the main span of a cable-stayed bridge becomes longer, the stay cables linking the towers and decks become more susceptible to vibrations induced by wind and traffic loads. Since these essential cables may experience tension loss for various reasons—and such losses can significantly degrade bridge performance, potentially even leading to collapse in extreme cases—effective methods of monitoring cable tension are indispensable. Various methods for monitoring cable tension have been studied and applied. Among them, the vibration-based method estimates tension using vibration data and has the advantages of easier installation and higher cost-effectiveness compared to other methods. As of 2022, approximately 260 cable tensiometers have been installed on cable-supported bridges managed by the Special Bridge Management Center of the Korea Authority of Land & Infrastructure Safety (KALIS), and most of these monitor cable tension by estimating it through vibration-based methods that use acceleration data. Vibration-Based Method The vibration-based method for estimating cable tension involves the following procedure: 1) installing an accelerometer on the exterior of the cable to continuously collect vibration responses (Figure 2, Step #01), 2) transforming the collected responses into power spectral density (PSD) signals in the frequency domain, 3) extracting peak information (fn: peak location, n: peak order) from the transformed PSD signals (Figure 2, Step #02), and 4) deriving a linear regression equation from the extracted peak information (Figure 2, Step #03). Using the intercept b of the regression equation (0.729 in Figure 2, Step #04), together with the cable’s properties—effective length (Leff) and unit weight (w)—the cable tension is then estimated as expressed in Equation (2). Since the excitation conditions of the cable are not constant, the data collected by accelerometers installed on the cable allow for a more stable detection of peak information as the measurement time increases. However, the longer the measurement time, the longer the tension estimation cycle becomes. Therefore, in practice, acceleration measurements are generally taken at a frequency of 100 Hz, with measurement durations of 10 minutes. The collected acceleration data are then transformed into the frequency domain, and peak information (peak position and order) is detected from the transformed frequency spectrum. During the tension estimation process, the critical task of detecting peak information is mainly performed manually. For example, if data are collected in 10-minute intervals over a 24-hour period, this yields 144 data sets. If accelerometers are installed on 8 cables of a single bridge, peak information must then be detected from a total of 1,152 data sets. Because the detection of peaks is carried out primarily by a human operator, the process is labor-intensive and subject to the operator’s subjective judgment, reducing objectivity. An alternative approach to manual detection is to use pre-set conditions. For instance, peaks can be identified by detecting locations where the amplitude exceeds a threshold, or by defining frequency bands where peaks are expected and selecting the highest value within that band. However, peak information may be missing depending on excitation conditions or cable damage. In cases where the natural frequency of the cable coincides with external excitation conditions, resonance may occur, resulting in unusually large peaks in certain frequency ranges. The limitation of methods based on automatic detection of pre-set conditions is that settings must be customized for each cable specification, and changes in spectral characteristics can hinder the accurate detection of peak information. IoT Measurement System with Automatic Peak Detection Algorithm The vibration signals of cable-stayed bridge cables, when transformed into the frequency domain, generate a power spectral density (PSD) that exhibits two distinct characteristics, as shown in Figure 3. First, the peaks in the cable PSD display a periodic pattern occurring at uniform intervals, reflecting the inherent dynamic properties of the cable. While the spacing of these peaks can vary depending on the cable’s specifications (such as material, geometry, and tension) and the overall structural system, periodicity with consistent intervals is a physical feature common to all cable members in cable-stayed bridges. The second characteristic is that the peaks have relatively higher amplitudes compared to surrounding frequency components. This means that, in the PSD, these peaks behave as outliers compared to neighboring values (Jin et al., 2021). To automatically detect such uniform peak intervals, one can apply the Automatic Multiscale-based Peak Detection (AMPD) technique, a biosignal processing method from the field of Biomedical Engineering (BME) (Scholkmanm et al., 2012). AMPD has the advantage of enabling complete automation because it can automatically detect periodically occurring peaks without any pre-configuration. To capture the second characteristic—where peaks appear as outliers compared to surrounding values—a threshold-based outlier detection method can be used in parallel. In this case, the threshold can be set using the Median Absolute Deviation (MAD) method, which is robust to data containing outliers (Rousseeuw et al., 1993). Based on the peak information estimated using these two techniques, the cable tension is calculated. This technology offers several advantages: (1) there is no need for pre-configuration, (2) it has a high robustness against signal variations, and (3) there is a low computational cost. Acceleration data for cable tension monitoring are mainly collected through wired measurement systems. In these systems, the sensors are connected to the data acquisition devices with cables, and the collected data are transmitted to the managing authority for use in tension analysis. Wired measurement systems have the advantage of enabling stable measurements without data loss; however, they involve additional costs due to the need for cabling between sensors and loggers as well as the installation of protective conduits to prevent disconnection, and they are limited in terms of installation locations and the number of sensors that can be deployed. In recent years, various IoT (Internet of Things)-based measurement systems have been developed and applied to facilities. However, most of them, like traditional wired systems, remain at the level of simply collecting and transmitting data. While this offers advantages in terms of installation flexibility and scalability, it does not fully utilize the potential strengths of IoT technology. IoT measurement systems can incorporate diverse algorithms to filter and process raw data before its transmission, rather than sending the raw data itself. This edge computing technology processes data in real time at the sensor terminal or adjacent devices, reducing the burden of transmission to servers and lowering both processing costs and time. By installing the previously described automatic peak detection algorithm on an IoT-based measurement system and applying it to cable-stayed bridge cables, a study was conducted to verify the algorithm’s accuracy, as well as the usability and efficiency of the measurement system. Through this research, the potential of applying IoT measurement systems and edge computing technologies to facility monitoring was confirmed. Epilogue The integration of IoT measurement systems with edge computing makes it possible to move beyond the traditional approach of transmitting large volumes of raw data to servers for collection and analysis, enabling on-site data processing and optimized management. With the advancement of data processing and analysis technologies now embedded into IoT measurement systems, the scope of data utilization in facility maintenance—which was previously limited to raw data transmission—is expected to expand significantly. In addition, with the advent of real-time processing, it is now possible to respond immediately rather than after the fact, making preventive maintenance achievable. This not only helps prevent safety accidents but also is expected to reduce both direct and indirect social costs. References 2024 Road Bridge and Tunnel Status Report. Jin et al. (2021), Fuly automated peak-picking method for an autonomous stay-cable monitoring system in cablestayed bridges, Autom. Constr. Vol. 126. Scholkmanm et al. (2012), An efficient algorithm for automatic peak detection in noisy periodic and quasiperiodic signals, Algorithms, Vol. 5. Rousseeuw et al. (1993), Alternatives to the median absolute deviation, J. Am. Stat. Assoc. Vol. 88 Department of Structural Engineering Research Date2025-09-24 Hit 115
• AI-Based GPR Data Analysis Technology for Detecting Underground Cavities and Buried Objects Research Fellow Lee Dae-young, Department of Geotechnical Engineering Research, KICT Prologue In recent years, a series of large-scale ground subsidence accidents have occurred in urban areas such as Seoul. Examples include the sinkhole accident in Myeongil-dong, Gangdong-gu, Seoul, and the underground collapse at the Sinansan Line construction site in Gwangmyeong. Following these incidents, the Seoul Metropolitan Government announced that it would be strengthening safety management against ground subsidence by conducting intensive Ground Penetrating Radar (GPR) surveys in the areas around excavation sites (Seoul City, 2025). Ground Penetrating Radar (GPR) is a geophysical survey method that uses electromagnetic waves to detect underground structures such as sewer pipelines, buried utilities, and cavities. Since the large-scale cavity incident at the Seokchon Underpass in 2014, GPR surveys have been actively applied to investigate subsurface cavities and ground subsidence beneath urban roads. As a non-destructive survey technique, GPR is useful for identifying underground utilities, cavities, and soil structures. However, it has several limitations, including depth restrictions depending on frequency, sensitivity to soil conditions, and difficulties in data interpretation. In addition, GPR analysis relies heavily on expert interpretation, and for high-resolution or 3D surveys, the data processing and interpretation require a significant amount of time, with notable variations in the reliability of the results. To address these issues, research is now underway on AI-based methods for automatically analyzing GPR data. This article introduces the principles of GPR surveys, along with AI-based methods for analyzing GPR data to improve the accuracy of interpretation, shorten analysis time, and enable real-time analysis. Principle of GPR Surveys Ground Penetrating Radar (GPR) is a survey technique that can identify the location and shape of underground structures such as buried pipelines through transmitting electromagnetic waves into the ground and receiving the reflected signals generated at the boundaries of such structures, while considering their different electrical properties (conductivity and permittivity). GPR employs radio waves with frequencies of several tens of MHz or higher, and is mainly used as a non-destructive testing method to investigate relatively shallow targets at depths of approximately 1–3 meters. It is applied to the detection of underground utilities, cavities, tunnel voids, and stratigraphic structures. More recently, GPR surveys have been intensively conducted in areas where ground subsidence is a concern due to aging sewer pipelines, serving as an evaluation method to help prevent ground collapse (Figure 1). In the analysis of GPR survey data, buried pipelines exhibit strong amplitudes and appear in the form of hyperbolae, as shown in Figure 2. While single-channel GPR systems using one transmitter and receiver pair have been mainly used, high-resolution three-dimensional multi-channel GPR systems have recently come into wider application. GPR surveys are effective for targets buried at shallow depths of up to approximately 3 meters, within which range most pipelines are located, but have limitations when it comes to deeper investigations, such as tunnel construction or large-scale excavation sites. GPR Data Using AI Techniques In the context of the Fourth Industrial Revolution, the outstanding performance and popularization of Artificial Intelligence (AI) technologies have further expanded their applicability. The application of AI to GPR analysis has potential to improve the accuracy and efficiency of underground structure detection and reduce interpretation errors. Recently, to address errors and technical challenges that arise during GPR image interpretation, research utilizing deep learning—one of the machine learning techniques widely applied in the field of image processing—has been actively conducted. The AI-based method for analyzing GPR data involves collecting GPR data in B-scan and C-scan formats, performing noise removal and corrections, and then carrying out data labeling. After generating a corrected labeled training dataset, a Convolutional Neural Network (CNN)-based AI algorithm is used for object detection (Girshick, 2014). Through deep learning, the reliability of buried pipeline detection can be significantly enhanced. The Korea Institute of Civil Engineering and Building Technology (KICT) has conducted research on the application of AI to improve the accuracy of GPR surveys for cavity detection in the ground and the investigation of underground obstacles beneath roads, with the aim of preventing ground subsidence. GPR survey data were used to detect buried pipelines and cavities, and high-quality labeled datasets were generated by converting the GPR data into images and removing noise such as clutter. For the detection of underground utilities and cavities, the Faster R-CNN algorithm was applied, and by employing various training techniques, optimal performance for detecting buried pipelines and cavities was achieved. Through this effort, the KICT developed AI algorithms and GPR data analysis technologies capable of detecting underground cavities and buried pipelines. Epilogue With the acceleration of urban development and the resulting increase in large-scale excavation works, as well as the occurrence of urban sinkholes caused by aging infrastructure, the use of GPR surveys for detecting cavities and ground subsidence has become increasingly important. Recently, research has been progressing on the application of AI technologies to advance GPR data analysis. Integrating AI into GPR surveys can reduce data processing time while improving the consistency and accuracy of interpretation results, thereby overcoming the limitations of traditional GPR analysis. AI-based automatic analysis technology also enables the real-time processing of GPR data and reduces interpretation errors, allowing decision-making processes to move more quickly. Ultimately, this technology can play a vital role in preventing ground subsidence accidents and enhancing the safety of underground utilities. References Seoul Metropolitan Government (2025). Special Countermeasures for Strengthening Safety Management Against Ground Subsidence at Large Urban Excavation Sites. Press Release, Road Management Division, Disaster and Safety Office, Seoul Metropolitan Government. Lee, Dae-young (2015). Development of Ground Subsidence Evaluation Methods Caused by Damage to Old Sewer Pipes. Proceedings of the Joint Conference of the Korean Society of Water and Wastewater (KSWW) and the Korean Society on Water Environment (KSWE), Special Session V-1. Lee, Dae-young (2018). Risk Assessment of Sewer Defects and Ground Subsidence Using CCTV and GPR. Journal of the Korean Geosynthetics Society (KGSS), Vol. 17, No. 3, pp. 47–55. Korea Institute of Civil Engineering and Building Technology (2022). Development of Smart QSE-Based Undergrounding Innovation Technology for Overhead Lines and Road Performance Restoration Technology (1/3), Annual Report. Korea Institute of Civil Engineering and Building Technology (2024). Development of Smart QSE-Based Undergrounding Innovation Technology for Overhead Lines and Road Performance Restoration Technology (3/3), Final Report https://ashutoshmakone.medium.com/faster-rcnn502e4a2e1ec6 R. Girshick, J. Donahue, T. Darrell, and J. Malik (2014) “Rich feature hierarchies for accurate object detection and semantic segmentation,” In Proc. CVPR. Department of Geotechnical Engineering Research Date2025-09-24 Hit 415
• Research Directions for Smart Road Infrastructure for Future Mobility Researcher:Ryu Seung-ki, Senior Research Fellow, Department of Highway & Transportation Research, KICT Prologue Currently, South Korea’s road infrastructure is suffering due to traffic congestion, environmental pollution, and various traffic accidents, all of which are the result of an imbalance between regional traffic demand and road supply. In addition, the roads are aging, and due to climate change, the rate of road deterioration is accelerating. With increasing socio-economic damages caused by these factors, it is clear that “smart” roads are needed to extend the service life of roads and maintain normalcy, and innovative research and development to realize this must be pursued. Smart roads are envisioned as future roads that can assess their current state based on past and present data and predict future conditions to proactively and quickly restore road abnormalities. Roads, being public goods, are spaces where risks will always exist, and thus substantial finance must be allocated for their maintenance and recovery to ensure their permanence and resilience. We must continue the efforts to supply smart road technologies by researching and developing optimal solutions. Smart roads can be realized by improving mobility for various modes of transportation, such as automobiles, railways, Urban Air Mobility (UAM), subways, and buses, enhancing connectivity between these modes, and introducing innovative transportation systems. The core technologies in realizing smart roads involve traditional road and transportation technologies, along with ICT convergence technologies such as AI, IoT, big data analytics, and V2X, with recent advancements in AI technology and its wide-ranging applicability making it an essential strategic technology for smart roads. Smart roads must focus on the development of core technologies for future mobility across the entire process, from planning and construction to maintenance. In the planning and construction stages, calculating, analyzing, and predicting materials, time, and costs can help manage resources efficiently while also saving costs. In the construction and maintenance stages, smart road technologies will play a critical role in quality and safety management. Future infrastructure research and development policies for roads must introduce more innovative research programs and government policies to address current traffic issues and respond to upcoming changes in future mobility. The Korea Institute of Civil Engineering and Building Technology (KICT) is engaged in research and development in the field of core technologies for future transportation infrastructure, with the Department of Highway and Transportation Research playing a central role. Research Directions and Achievements in Smart Road Infrastructure From 2021 to 2024, the Department of Highway and Transportation Research has been focused on developing core technologies for smart road infrastructure for future mobility. The key research areas have been set as future mobility, sustainable and eco-friendly roads, international and regional cooperation, and the construction of future road demonstration infrastructure. Notably, the future mobility sector has been focused on developing autonomous cooperative driving infrastructure and service technologies, with research and development centered around road infrastructure. In 2021, the first year of the Department of Highway and Transportation Research, research planning focused on road facility safety, digital transition services, traffic signal systems, active road icing accident reduction, wireless charging energy road infrastructure, smart mobility MaaS (Mobility as a Service), and AR-based vehicle location recognition technologies. In 2022, representative and seed projects were actively conducted. Research and development were focused on technologies for safe future roads, road infrastructure for autonomous driving safety, and driver assistance technologies based on vehicle video recorders. At the same time, tasks were planned for intelligent road safety management systems and public transportation infrastructure service diagnosis technologies, in response to policy demands. In 2023, the department continued to support representative projects and initiated planning for tasks such as digital twin services for road risk management, smart parking platforms, and vehicle tire data-based road information services. In 2024, new core projects were launched, including AI Safe Road technology for next-generation neighborhood environments and plastic road infrastructure technology for autonomous driving. Seed projects such as video-based road risk element detection technology, parking lot digital transition technology, and road traffic noise model design were also pursued. While much research and development have been conducted for high-spec trunk roads such as highways and national roads, research and development on local roads, narrow roads, and mixed-use pedestrian and vehicle roads have been relatively scarce. Moreover, the ultimate goal of future mobility will be a safe last-mile autonomous driving service. Smart road infrastructure for future mobility can be realized through a variety of core technologies, and we are proactively researching these to ensure their integration into follow-up projects, practical application, and responsiveness to policy demands. In particular, increasing the utilization of artificial intelligence (AI) is crucial. To achieve this, we are developing various AI applications and core technologies that integrate AI into existing road systems. Summarizing the achievements of the Headquarters Purpose-Specific R&R projects carried out from 2021 to 2024, the Purpose-Specific R&R projects played a pivotal role in paving the way for large follow-up projects, yielding excellent results. Over the three years, 12 subprojects under the Purpose-Specific R&R initiative were connected to 17 follow-up projects, ensuring continuous research and development. Achievements in the Development of AI-Based Road Infrastructure Application Services for Future Mobility In the course of the Purpose-Specific R&R projects, we would like to introduce the AI application mobility service technology for smart road infrastructure, which we have secured as part of future mobility. First, we present a solution to accidents related to road potholes, which have been repeatedly identified as high-risk objects on the road. Autonomous vehicles continue to face challenges in detecting high-risk objects such as potholes using visual recognition. Solutions for detecting such difficult objects require highly reliable detection performance. We have developed the first domestic AI-based road pothole detection solution using black box video footage, and through ongoing research and development to enhance object detection performance under limited perception conditions, are striving to create the world's highest-performing solution. If an AI application solution is secured that can achieve a high level of performance in detecting and recognizing high-risk objects and perception limitations on the road, smart road infrastructure will be able to automatically identify damages such as cracks, subsidence, and potholes on the road. This will lead to efficient maintenance, and transform the infrastructure into a future mobility system that collaborates with autonomous vehicles. Next, residential area roads involve various dynamic objects moving simultaneously, such as pedestrians, vehicles, and motorcycles. These situations will pose a challenge for fully autonomous driving services. Therefore, a solution is needed that enables the smart road infrastructure to recognize these dynamic objects and collaborate with autonomous vehicles. We have proactively developed a multi-object classification solution for narrow roads with a mix of dynamic objects. This solution allows smart road infrastructure to autonomously detect the presence of dynamic objects, their movement trajectories, and parked objects, while also classifying them. Narrow roads, such as alleys, are often obstructed by illegally parked vehicles or other obstacles, making it difficult to assess potential blockages or inaccessible routes for emergency vehicles like fire trucks and ambulances. This can lead to a failure to achieve the critical "golden time" needed to reach the destination, thus increasing the risk of damage. We have developed a smart road infrastructure solution that predicts the effective lane width or accessible routes for narrow roads to secure the golden time. This solution can be applied to video-based traffic accident risk prediction and information provision services. It detects the boundaries of various objects on narrow roads and excludes the objects occupying the road surface to calculate the actual effective road area. By calculating the effective lane width frame by frame, this solution can provide real-time information to emergency vehicles and administrative authorities. For fully autonomous driving, it is essential to secure traffic signal detection and classification technology based on vehicle-mounted cameras. Autonomous vehicles must detect traffic signals ahead and recognize their signal states using visual sensors. To ensure the highest level of autonomous driving safety, fully autonomous vehicles need to use image sensor data to accurately detect traffic signal objects and classify them by type. However, there are still significant challenges in detecting traffic signals when the signal object is small relative to the background or when the contrast with the background is low. We are developing a solution to address these difficult-to-detect recognition issues and improve performance in challenging situations. Research and Development Policies on AI in the US and China The United States recognizes artificial intelligence (AI) technology as a strategic technology directly linked to national security, and is promoting related policies. Under the Biden administration, executive orders were issued at the federal level to develop and spread trustworthy AI, while strengthening international cooperation in response to the emerging potential risks of AI. The policy to ensure the safety and reliability of AI as a national security technology is expected to continue during the Trump 2.0 era under the National AI Initiative Act (2020). Korea must prepare an AI strategy to respond to the Trump 2.0 era. US initiatives in this period can be expected to focus on emphasizing the safety and reliability of AI technology, while maintaining US global leadership through the reinforcement of export controls and technology management related to national security. In response, Korea needs to adopt a balanced policy that proactively addresses global regulatory environments while aligning with the US-led competitive framework for AI technology development and industrial promotion. Strengthening technological alliances with the US and ensuring the ethical use and reliability of AI technology while harmonizing with international regulations is essential. In addition, domestic policies such as the "BASIC ACT ON AI" must be designed to align with global standards, supporting the international expansion of domestic companies and minimizing the impact of the US's export controls and strengthened technology management. At the same time, to secure independent competitiveness in AI technology, it is crucial to increase national investment in research and development (R&D), strengthen global cooperation networks, and foster a comprehensive strategy to support the domestic startup and corporate ecosystem. According to the draft of the 2024 budget report submitted to the Annual Session of the National People’s Congress, China has allocated 371 billion yuan for scientific and technological R&D, with 98 billion yuan (approximately KRW 18 trillion) specifically earmarked for basic science research in fields like physics and chemistry. The Chinese government is emphasizing a "scientific and technological revolution" and is focusing on the development of technologies such as the 72-qubit superconducting quantum computer, hydrogen energy, commercial aerospace technology, robotics, and artificial intelligence (AI). Amid escalating tensions as the US restricts China’s access to key technologies like semiconductors, AI, and quantum computing, China appears determined to avoid falling behind in the global power struggle by expanding its investment in science and technology. Furthermore, China believes that "high-quality development" is a prerequisite to achieving stable growth, viewing independent and innovative science and technology as a driving force of national growth. Recently, China’s AI company DeepThink gained attention by achieving results comparable to OpenAI's models. As scientific and technological innovation is the foundation for the growth of major powers, we must review and adjust our research and development directions accordingly. Epilogue The research and development of smart road infrastructure for future mobility should focus on integrating autonomous vehicles with smart roads, advanced road safety services, and AI-powered smart roads. Core technologies for smart road infrastructure require policy enhancements such as data sharing, standardization, and the expansion of the data application industry to improve efficiency and safety. The Department of Highway and Transportation Research should concentrate on developing core technologies for smart road infrastructure that collaborate with future mobility. This requires the building of data-driven analytical capabilities specific to road traffic, which in turn will help develop internal capabilities for utilizing road traffic infrastructure-based AI technologies, enabling long-term growth and adaptation to government policy changes. Furthermore, to strengthen global competitiveness, it is essential to engage in international cooperation projects and collaborative research initiatives, as well as to expand proof-of-concept research on societal issues by utilizing real-scale smart road infrastructure test beds. In the future, smart road infrastructure will enhance mobility, accessibility, convenience, and safety through cooperative operation between smart roads and autonomous vehicles. To effectively respond to the emergence of future mobility, it is crucial to continuously develop and prepare core technologies for smart road infrastructure. References Ryu Seung-ki et al. (2024) Development of Core Technologies for Future Smart Transportation Infrastructure, Korea Institute of Civil Engineering and Building Technology (KICT) Lee Hae-soo & Yoo Jae-hong (2025) Current Status and Implications of U.S. Artificial Intelligence (AI) Safety and Reliability Policies, Software Policy & Research Institute (SPRi) Department of Highway & Transportation Research Date2025-06-23 Hit 960
• Research on Nano-Scale Material Evaluation Methods for Securing Advanced Construction Material Design Technologies Research on Nano-Scale Material Evaluation Methods for Securing Advanced Construction Material Design Technologies ▲ Research Fellow Yun Tae-young, Department of Highway & Transportation Research, KICT R&D Methods in Advanced Materials In the advanced materials fields such as biotechnology, chemical engineering, semiconductor and battery technologies, where accuracy and speed are core factors in materials development competitiveness, there has been a shift away from traditional trial-and-error experimental methods. Instead, computational science and material informatics are now actively utilized for materials development. This approach replaces simple regression analysis methods that identify correlations between material names or compositions and their properties with the establishment of Quantitative Structure and Property Relationships (QSPR), which predict material properties by utilizing molecular structural characteristics, composition, and interactions. The key difference between these approaches is that while regression analysis methods cannot predict properties for materials not included in the database, QSPR methods can predict the properties of materials with similar molecular structures or molecular bonding characteristics, even if they weren't included in the original database. Figure 1 conceptually illustrates the differences and relationships between traditional research and development methodologies and recent research and development approaches for materials development. Today, material research and development no longer relies solely on limited data obtained through experiments. The molecular structure and composition of materials for development are used to calculate the energy required for molecular bonding and separation through molecular dynamics or quantum mechanics. These dynamic theories can also be applied to calculate various physical properties, such as density, elastic modulus, viscosity, solubility, and adhesion strength. Figure 2 shows the solubility and adhesion strength resulting from interactions between non-crystalline and crystalline structures within materials, using molecular dynamics or quantum mechanics. Application of Nanoscale Material Development Methods to Construction Materials With the growing demand for higher safety, methods for evaluating the structural and functional adequacy of construction materials are becoming more refined. In addition, the growing interest in the environmental impact of construction materials and improved performance has made the development process for new materials more complex. For example, while simple engineering properties were previously used to evaluate asphalt binders for road pavements, mechanical properties, such as viscoelasticity and elastic recovery—which require complex equipment and theoretical understanding to ascertain—have been utilized since a U.S. research program was proposed in 1987. Furthermore, as heating materials like carbon nanotubes and graphite are being considered as road additives for snow melting functions, the complexity of experiments for evaluating materials performance is expected to increase. Highly complex materials with diverse functions tend to show sensitivity in their evaluation properties depending on experimental methods and procedures. Consequently, efforts to compensate for method and procedure-related issues, such as preferring experimental evaluations on full-scale components, significantly increase evaluation time and costs. Nanoscale material evaluation methods that predict properties based on computational data utilizing nanoscale molecular structure and composition are relatively highly efficient in terms of time and cost. Figure 3 shows various qualitative variables that can be considered when using molecular dynamics for the asphalt mixtures used in road pavements, including material and additive types, aging effects, and moisture content. These qualitative variables are broken down into special quantitative information, such as molecular structure composition, and are used in machine learning along with properties like solubility, adhesion energy, tensile strength, viscosity, and elastic modulus to design new construction materials or predict the properties of new designs that weren't used in the training process. Future of Nanoscale Construction Material Development Methods and Construction Material Technology In the past, South Korea's construction technology development strategy took a “fast follower” approach, with companies quickly adopting and internalizing technologies first developed in advanced countries. This fast follower strategy is expected to continue due to Korea’s cultural characteristics that expect quick results, constraints on national budget, an economic-centered selection and concentration logic resulting from cultural characteristics, and limitations on the expandability of the domestic construction market. However, in a situation where information has become generalized and barriers between fields are lowering due to the universalization of convergent scientific and technological development methodologies, the construction field's technology development cannot continue to emphasize only system integration roles. To provide essential construction technologies to the public in a timely, efficient, and stable manner, which primarily serves the national interest, technology development in construction areas that are difficult for other fields to approach—due to low added value or high entry barriers to expertise—is necessary. It is anticipated that if nanoscale construction materials development technology, which is difficult to approach from other fields due to low homogeneity and complex environmental applications, is successfully implemented, it could become a good example of system integration including core technologies in the construction field. References Yun Tae-young (2024) Research Methods Using Materials Informatics and Molecular Dynamics for the Development of Road Pavement Materials (I). Korean Society of Road Engineers (KSRE) v. 26, no. 4, pp. 45-58. Yun Tae-young, Moon Jae-pil, Shim Seung-bo, Joo Hyun-jin (2024) Genetic Algorithm–Partial Least Squares Regression Model for Predicting Density from Asphalt Binder Molecular Descriptors. Korean Society of Road Engineers (KSRE) v.26, no.4, pp.69-78. I. Jeon, J. Lee, T. Lee, T. Yun, S. Yang (2024) In Silico Simulation Study on Moisture-and Salt Water-induced Degradation of Asphalt Concrete Mixture, Construction and Building Materials v.417. Department of Highway & Transportation Research Date2025-03-26 Hit 973
• Development of 3D Printing Materials for the Automation of Underwater Construction Development of 3D Printing Materials for the Automation of Underwater Construction ▲ Research Specialist Seoh Eun-a and Senior Researcher Lee Ho-jae, Department of Structural Engineering Research, KICT Prologue In 2024, the World Meteorological Organization (WMO) announced through its "State of the Global Climate 2023" that global sea levels have been rising at a rate of 4.77 mm annually over the past decade, more than twice as fast as previous periods, while average sea surface temperatures reached record highs. With 40% of the world's population living within 100 km of coastlines, these rising sea levels mean that people are facing immediate challenges in securing living spaces and survival. As the need to secure marine resources and expand living areas increases, demand for coastal spaces is growing, with development expanding into underwater spaces. In Guam, Dubai, and the Maldives, underwater hotels and resorts have been built at depths of 5-6 meters, and are currently operational. In Korea, Hyundai E&C and the Korea Institute of Ocean Science & Technology (KIOST) have formed a consortium to build an underwater science base in the sea off Ulju-gun, Ulsan, with completion targeted for 2026. Furthermore, the development of new underwater construction technologies is expected to increase the construction of facilities for rapid recovery from natural disasters such as typhoons and earthquakes, which are becoming intensified by abnormal climate conditions, as well as preventive facilities from a disaster prevention perspective. Although demand for underwater structures is increasing, practical difficulties exist in the construction and quality control of underwater projects. In particular, the underwater construction field faces increasing safety risks due to a shortage of divers and the aging of the existing diver work force, creating a growing demand for underwater construction automation technologies. There is also an increasing demand for automation technologies for the repair and reinforcement of various underwater structures, such as water intake and discharge structures, dams, and underwater portions of bridges. When constructing underwater structures, high-performance structural and material technologies that can withstand water pressure are essential, and there also are challenges related to weather conditions. To overcome these environmental challenges, the application of technologies that minimize diver deployment through the use of underwater robots is expanding. Underwater robots can work even in strong currents, solve problems that are difficult for humans to address underwater, and improve the accuracy of construction through real-time filming and sensing technologies. KIOST opened the Underwater Construction Robotics R&D Center in 2013 in response to social demands for underwater construction automation. This R&D Center developed robotic technologies for six years until 2019, and pursued demonstration and dissemination projects for four years until 2022. In particular, the Underwater Construction Robotics R&D Center developed three types of robots: a light-duty swimming ROV (Remotely Operated Vehicle), a heavy-duty swimming ROV, and a track-based robot for underwater welding, subsea cable burial, underwater structure installation, and pipeline burial work. Due to the absence of on-site construction technology for underwater structures, 3D printing technology for underwater construction emerged as an automation solution to address this technical gap, as construction 3D printing technology is known to be applicable in extreme environments like space habitat creation. With recent global initiatives for underwater city development and the construction of offshore structures connected to underwater environments, research on underwater construction 3D printing technology is expanding worldwide. This article briefly discusses the current status and direction of development of 3D printing materials for underwater construction automation. Required Performance of 3D Printing Materials for Underwater Construction Concrete is a material that requires sufficient flowability to fill formwork properly. However, materials for 3D printing need to have the opposite characteristics—they must maintain their shape after extrusion, and resist deformation from external forces as they are continuously stacked. Generally, construction 3D printing technology consists of three stages: production, pumping, and printing. Since cementitious materials decrease in flowability and harden over time, material development must be aligned with the entire system, from production to printing. If materials harden too quickly, blockages can occur inside the equipment; on the other hand, if flowability decreases too slowly, the materials will not stack properly. Because cementitious materials are difficult to control even with subtle changes in raw materials and are sensitive to temperature and humidity changes, the development and property control of construction 3D printing materials is the most difficult and critical technology. Concrete is a mixture of cement, water, sand, and gravel, taking at least 8 hours to harden after mixing. Since components can be washed away when in contact with water before hardening, placing concrete underwater presents technical challenges. For underwater construction 3D printing materials, printability, stackability, dimensional stability, and dynamic performance are key. The transportation and printing performance of 3D printing materials can be quantified through flowability and rheology evaluation, and the application of undersegregation water anti-cementitious materials is essential. Generally, to ensure layer stackability and dimensional stability, a method of adjusting the nozzle movement speed (stacking speed) according to the stacking path is used. Most construction 3D printing materials use mortar, a mixture of water, cementitious materials, and fine aggregates. For 3D printing technology to be practically usable, faster printing speeds than current mortar 3D printing technology are required, as mortar 3D printing has limitations when it comes to improving the one-time printing area of a layer. However, 3D printing materials mixed with coarse aggregates have an advantage in that the coarse aggregates within the layers provide support and friction, making it easier to increase layer height. Accordingly, research on technologies applying coarse aggregates to aerial and underwater 3D printing for construction has been expanding globally since 2020. Performance Verification of 3D Printing Technology for Underwater Construction From 2020 to 2022, the Korea Institute of Civil Engineering and Building Technology (KICT) conducted research on "Development of Concrete Composite Materials for Underwater Stacked Placement." Through this research, concrete composite materials with an underwater stacked compressive strength of 30 MPa or more and an underwater/aerial compressive strength ratio of 80% were developed, along with underwater concrete stacking experimental equipment capable of achieving a single layer height of 50 mm and a layer width of 100 mm. The developed underwater construction 3D printing technology underwent performance verification experiments in a large water tank and at the KICT's breakwater testing facility. Concrete specimens were produced under identical conditions in both underwater and aerial environments using the developed underwater construction 3D printing technology, and layer width and height, total height of the stacked structure, and compressive strength were measured. In a static water tank environment, the underwater stacked concrete achieved a compressive strength of 62.8 MPa, which was 99% of the aerial compressive strength. The underwater printed components had a layer width of over 100 mm, a stacking thickness of 52.9 mm, and a deflection relative to the total height of the component of only 1 mm, demonstrating high dimensional stability (Figure 1). In addition, the performance of the underwater 3D printing material was evaluated under conditions simulating the average flow velocity of Busan New Port at the KICT's breakwater testing facility. During the printing stage, there was minimal separation of the 3D printing material, which maintained its shape during printing and achieved the target turbidity standard of 50 mg/l (Figure 2). Through these tests, it was confirmed that direct construction of structures using 3D printing technology is possible in underwater and current environments. Epilogue Underwater construction 3D printing technology is poised to become a key proprietary technology in the evolving field of construction convergence, with potential applications in extreme underwater environments like underwater cities and deep-sea bases. However, before it can be widely implemented in underwater construction projects, several practical challenges must be addressed. Currently, the technology shows promise in several areas, including creating artificial reefs tailored to support the growth of seaweed and shellfish, manufacturing custom replacement parts for damaged underwater structures, and building underwater structures to prevent coastal erosion. Furthermore, the development of repair and reinforcement technologies could lead to automation in underwater structure maintenance. With continued advancements, underwater 3D printing technology is expected to play a significant role in the repair and construction of underwater structures, opening the door to more efficient and scalable solutions in the field. References World Meteorological Organization (2024) State of the Global Climate 2023, 1347, 3-7. Korea Institute of Marine Science & Technology Promotion (KIMST) under the Ministry of Oceans and Fisheries (2023), Final Report on the Development of Concrete Composite Materials for Underwater Stacked Placement Department of Structural Engineering Research Date2025-03-26 Hit 690
• No more carbon emissions! The era of Carbon dioxide-eating concrete is here! No more carbon emissions! The era of Carbon dioxide-eating concrete is here! - Evelopment of carbon dioxide-storing concrete technology utilizing CO2 nanobubble mixing water - CO2storage capacity of 1.0ￚ1.8 kg per 1 ㎥ ready-mixed concrete The Korea Institute of Civil Engineering and Building Technology (KICT) has become the first Korean organization to develop "Carbon Eating Concrete (CEC) using Nanobubbles," a groundbreaking technology that stores carbon dioxide (CO2)—a major contributor to global warming—within concrete using nano-bubbles. Concrete is the most widely used artificial material worldwide, with an annual production volume of approximately 30 billion tons. As demand for urbanization and infrastructure grows, so does concrete usage. Despite being a single material, the process of concrete production (including cement manufacturing) accounts for about 5% of global greenhouse gas emissions due to the significant CO2 emissions involved. "Carbon Capture Utilization for Concrete (CCU Concrete)" technology is concrete produced by utilizing CO2 in a manner that does not impact climate change. In a paper published in Nature Communications in 2021, it was estimated that CCU concrete could theoretically sequester 0.1ￚ1.4 Gt of CO2 by 2050. CCU concrete is recognized as the only technology capable of mineralizing captured CO2 through reaction with concrete, thereby storing CO2 stably inside the material without re-releasing it into the atmosphere. Typically, concrete undergoes carbonation when exposed to atmospheric CO2 lowering its internal pH and losing alkalinity. While atmospheric CO2 concentration is very low at 400 ppm, causing this carbonation process to proceed extremely slowly, it puts the reinforcing steel surrounded by low-durability concrete at an increased risk of corrosion. However, CCU concrete technology intentionally induces a reaction between high-concentration CO2 and the internal concrete materials. Through this chemical reaction, the CO2 is converted into carbonate minerals that enhance strength, permanently storing it within the concrete. Consequently, these carbonate minerals increase the microstructural density, enabling the production of concrete with improved strength and durability compared to conventional concrete. In other words, CCU concrete is not merely a CO2 storage solution but offers additional benefits like enhanced concrete performance and reduced cement usage, indicating significant market potential. The research team at the Department of Structural Engineering Research of the KICT has thus developed the first "Carbon Eating Concrete (CEC)" in Korea, which can effectively absorb and store carbon dioxide in concrete structures while simultaneously improving concrete's compressive strength and durability using nanobubbles. Concrete is traditionally manufactured by mixing cement powder, water, and aggregate. The research team developed CO2 nanobubble water, which is capable of storing high-concentration CO2 even under standard atmospheric conditions. "CO2 nanobubble water" is water containing numerous nanobubbles with high CO2 dissolution. The developed technology utilizes CO2 nanobubble water and industrial by-products in concrete production instead of regular mixing water. Advanced analytical techniques (Raman spectroscopy) verified the chemical interaction between CO2 in nanobubble water and concrete. The developed technology allows the direct storage of 1.0-1.8 kg of CO2 per 1 m³ of concrete production, comparable to the CO2 storage volume achieved by "Carbon Cure," a world-leading direct injection technology company from Canada. In addition, the research team developed "CEC" by applying optimal temperature and humidity conditions and mixing techniques using high CO2-reactivity industrial by-products to reduce cement usage. The developed CO2curing technology can maximize concrete's physical performance while minimizing the amount of cement required. Compared to traditional steam curing, it consumes less energy during production and achieves equivalent or superior compressive strength through CO2 curing techniques. A significant advantage is its high CO2 storage efficiency. To simulate CO2 curing environments under various temperature and pressure conditions, the team established Korea's largest high-temperature, high-pressure CO2 curing system for concrete. This achievement was developed through the major KICT project "Development of Eco-Friendly Carbon Eating Concrete (CEC) Manufacturing and Utilization Technology (2022ￚ2024)," supported by the Ministry of Science and ICT. Department of Structural Engineering Research Date2024-12-27 Hit 1833
• Developing Demand-Responsive Mobility Services Based on Autonomous Driving Technology: A Vision for the Future of Public Transportation Developing Demand-Responsive Mobility Services Based on Autonomous Driving Technology: A Vision for the Future of Public Transportation ▲ Research Specialist Jang Ji-yong, Department of Highway & Transportation Research, KICT Prologue The city of Seoul began its operation of autonomous buses in Cheonggyecheon in November 2022, which was followed by the launch of late-night autonomous buses running between Hapjeong Station and Dongdaemun Station in December 2023. Both of these were public transportation services provided along limited, fixed routes, with a driver's seat and a driver on board, yet are examples of commercialized public transportation services leveraging autonomous driving technology at the local government level. In the past, services such as Hyundai's "Shucle," "Zero Shuttle" in Pangyo, and "Majung" in Siheung in Gyeonggi Province have been trialed, though these were more akin to pilot operations. It seems that the autonomous driving technology we are approaching may first be experienced by most of us through public transportation. Public transportation, a service relied on by many for mobility within a city, provides greater convenience to the public as its service area expands. However, issues such as manpower and budget constraints impose limits on expanding the service area beyond a certain level. As one alternative in the public transportation sector, Demand Responsive Transit (DRT) services have been expanded to improve the quality and utility of public transportation services (Korea Research Institute of Transportation Industries, 2024). However, even DRT-based services cannot be completely free from operational manpower and financial constraints. For this reason, there has been active research into combining autonomous driving technology with the demand-responsive public transportation services attempted by Seoul and other local governments as an alternative that can overcome the inherent limitations of conventional public transportation. Advanced autonomous driving technology does not require drivers, which means that it can potentially be used to overcome some of the current limitations of public transportation, at least in terms of operational manpower and related financial constraints. Since April 2021, the Korea Institute of Civil Engineering and Building Technology (KICT) has been conducting a national research and development project called “Development of Real-Time Demand-Responsive Autonomous Public Transportation Mobility Service Technology” (Principal Researcher: Moon Byung-sup, Senior Research Fellow) to develop a public transportation mobility service utilizing autonomous driving technology. The goal is to develop a demand-responsive autonomous public transportation service that expands the service concept of existing public transportation, including DRT. This paper introduces what differentiates this service from existing ones and why it is called a "Vision for the Future of Public Transportation.” Definition of Demand-Responsive Autonomous Mobility Services This service is a demand-responsive public transportation mobility service based on autonomous driving technology. It aims to provide a first-and-last-mile service using Level 4 autonomous vehicles as defined by the Society of Automotive Engineers (SAE), transporting passengers to their desired destinations without fixed routes (Figure 1). To enable a safe public transportation service, a small vehicle equipped with a Level 4 autonomous driving system is being developed for demand-responsive service. What distinguishes this system from previous similar demand-responsive services is its ability to learn and remember individual users' travel patterns. Using this learned information, it generates optimal dynamic routes considering real-time changes in road and traffic conditions, and transports passengers accordingly. To provide this service, a 9-seater small vehicle is being made, allowing ride-sharing within pre-allocated routes and travel time allowances. The features of learning individual travel patterns to predict usage demand and preferred routes and proposing these to users, along with the capability for ride-sharing in an autonomous bus, clearly differentiate this service from previous offerings, making it a new vision for the future of public transportation. Configuration and Functions of Demand-Responsive Autonomous Mobility Services To provide a safe and comfortable demand-responsive public transportation service using small buses equipped with Level 4 autonomous driving systems, a central system responsible for service operation and control is required. Additionally, as this is a public transportation service based on autonomous driving technology, an evaluation system is required to assess the service’s public availability and operational efficiency. In addition to the autonomous small bus, central system, and evaluation system, facilities for vehicle storage and charging are needed. The system configuration for providing a demand-responsive autonomous public transportation mobility service is shown in Figure 2. The core functions for providing demand-responsive autonomous mobility services are included in the central system, vehicles, and user mobile app (Figure 3). First, a user mobile app is required to provide a public transportation service based on a driveress Level 4 autonomous system. The mobile app has functions for service requests, user authentication, billing, and checking reservation and operation information. The central system is responsible for the core functions that enable demand-responsive services. This involves algorithms that analyze passengers' travel history to predict call demand and pre-allocate the required number of vehicles to service areas. It also includes algorithms for selecting the nearest virtual stop to the user's call point. Additionally, the system generates optimal dynamic routes from origin to destination, reflecting real-time road and traffic conditions, and updates routes with minimal detour time when ride-sharing requests are made. The vehicle itself is equipped with an autonomous driving system, an in-vehicle terminal for user authentication, and a human-machine interface for interaction between onboard safety personnel and the autonomous driving system. The central system and vehicles exchange Travel Information Messages (TIM), Waypoint messages, and Probe Vehicle Data (PVD) in real time to provide services. Here, PVD is a message that contains the vehicle status information, including the driving trajectory of an autonomous small bus. The Waypoint message is a core message for implementing driverless autonomous public transportation services. It contains global path information representing the vehicle's route of movement and essentially includes the coordinates of nodes the vehicle passes through and the Estimated Time of Arrival (ETA) between nodes. Efforts to Develop Future Public Transportation Services Level 4 autonomous driving implies a "Mind-off" state, wherein the human driver is not required to be aware of the surroundings, make driving decisions or control the vehicle. Since public transportation services that apply driverless autonomous driving technology cater to a large number of users, the development of the service itself is important, but it is equally crucial to develop thorough verification technologies. Looking at previous research related to Autonomous Mobility-on-Demand (AMoD) services utilizing autonomous driving technology, most studies have only performed performance checks of the developed systems (Zhang et al., 2016; Barbier et al., 2019). To ensure passenger safety and successful establishment as a public transportation service, I am developing new service verification techniques by incorporating traffic engineering theories into the unavoidable verification technology development (Jang et al., 2023). Despite being public transportation, this world-first service concept learns individual travel patterns to predict usage demand and preferred routes in advance, and proposes them to users. It is an autonomous public transportation service that allows ride-sharing while following dynamic routes without fixed lines. Along with the development of autonomous public transportation service verification technology that considers public safety, these advancements are expected to lead a new future of public transportation that we will soon experience. ――――――――――――――――― References • Korea Research Institute of Transportation Industries (2024) Bus Transportation, Vol. 81, pp. 24-37. • Barbier, M., Renzaglia, A., Quilbeuf, J., Rummelhard, L., Paigwar, A., Laugier, C., Legay, A., Ibanez-Guzman, J., and Simonin, O. (June 2019), Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking. 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, pp. 252-259. • Zhang R., Rossi, F., and Pavone, M. (May 2016) Model Predictive Control of Autonomous Mobility on Demand Systems. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, pp.1382-1389. • Jang, J., Moon, B., and Ha, J. (2023) Development of Performance Verification Methodology for Level 4 Autonomous Driving Technology-based Demand-Responsive Mobility System. International Journal of Highway Engineering, 25(6), pp. 357-367. Department of Highway & Transportation Research Date2024-09-26 Hit 1373
• Development of Digital Image-Based Soil Color Assessment Technologies Development of Digital Image-Based Soil Color Assessment Technologies ▲ Senior Researcher Kwak Tae-young, Department of Geotechnical Engineering Research, KICT Prologue Soil color is widely used as a fundamental indicator for classifying and predicting soil properties, as it is known to be influenced by factors such as mineral composition, organic content, moisture content, and ion concentration, among others. Particularly in the field of agriculture, soil color is utilized as a prominent indicator for classifying soils, and suitable farming practices and crop types are determined based on soil color variations. Additionally, in civil engineering, the color of soil samples collected during soil surveying of a site is recorded in the boring log. This practice is based on the understanding that soils with similar colors in adjacent areas are highly likely to have similar geotechnical properties. Color is typically determined through visual observation. The MUNSELL Soil Color Charts shown in Figure 1 were developed to objectively differentiate observed soil colors based on combinations of hue, value, and chroma. However, the method of determining soil color using MUNSELL Soil Color Charts has the following limitations: ① It is susceptible to the subjectivity of the observer, ② the color of soil samples and the standard color chips can vary depending on environmental conditions like lighting, and ③ the standard color chips are discontinuous, making numerical or statistical analysis challenging. Recently, digital image-based soil color assessment technology has been highlighted as a means of overcoming these limitations. Digital image processing involves a series of computer-based processes to analyze digital images, allowing for rapid and objective soil color determination without the need for observer involvement. Furthermore, since soil color is represented as continuous values in digital image-based systems, it offers the advantage of enabling numerical or statistical analysis. Current Status of Development of Digital Image-Based Soil Color Assessment Technologies Current Status of Development of Digital Image-Based Soil Color Assessment Technologies Variations in Soil Color Due to Changes in Lighting Conditions Figure 2 presents digital images of granitic soils in the Anseong area, captured under lighting conditions simulating natural light. Despite capturing consistently prepared soil samples with the same camera settings, the soil color displayed in the images varied significantly based on the lighting's color temperature and illuminance. Color temperature is a measure of the color of light sources expressed in absolute temperature (K). The lower the color temperature, the redder the light source; the higher the color temperature, the bluer the light source. Illuminance is a measure of the intensity of light received on a specific surface. As illuminance increases, the light source becomes brighter. Soil color exhibited a similar trend to changes in color temperature and illuminance of the lighting. The phenomenon of soil color changing with lighting conditions highlights the clear limitations of previous studies that were not applicable in practical field settings. It is believed that the development of a digital image-based soil color analysis method that can consider irregular lighting conditions would further enhance the universality and applicability of research findings in practical field settings. Development of Digital Image Processing-Based Soil Color Analysis Technology A color system is a method of numerically representing colors, expressing a specific color as a point in a color space. There are various ways to define a color space, depending on the color system used. Some common color systems include RGB, HSV, CIEXYZ, CIExyY, CIELAB, and CIELUV (Billmeyer and Saltzman, 1981). In this study, two color systems, RGB and CIELAB, were utilized for soil color analysis. The RGB color system is the method most widely used in electronic devices such as digital cameras, and represents colors using the three primary colors of light: red (R), green (G), and blue (B). The RGB color system has the advantage of being able to reproduce most colors through a simple combination of the three colors. However, it cannot represent all the colors that the human eye can perceive. To overcome this limitation, the International Commission on Illumination (CIE) proposed the CIELAB color system based on the CIEXYZ color system (CIE, 1978). In the CIELAB color system, colors are expressed as a combination of L*, a*, and b*. L* represents the brightness of the color and ranges from 0 (dark) to 100 (bright). Additionally, a* and b* represent color values, and a* represents which side of red (positive number) and green (negative number) it is closer to, while b* indicates which side of yellow (positive number) and blue (negative number) it is closer to. Color System for Digital Image-Based Soil Color Analysis In an attempt to overcome the limitations of previous researches, the Korea Institute of Civil Engineering and Building Technology (KICT) has developed a digital image processing-based soil color analysis technology that can consider irregular lighting conditions in the field. As shown in Figure 3, a digital image capture studio was established to simulate natural light conditions. Various soil samples, including a single silica-based sand sample and granitic soil collected from four different regions, were photographed under different lighting conditions. Digital image processing was performed on the captured sample images to extract and analyze soil color in various color systems (RGB, CIELAB). In the RGB color system-based soil color analysis, it was observed that as the illuminance of the lighting intensified, the soil color components (R, G, B) also increased. Of the RGB components, green (G), which is known to have the highest correlation with brightness, showed a very high correlation with illuminance. However, red (R) and blue (B) showed relatively lower correlations due to the influence of color temperature. Since soil color represented in the RGB color system is influenced to some extent by both illuminance and color temperature, it was considered challenging to completely exclude (or correct for) the effects of lighting conditions using this system. The analysis of soil color based on the CIELAB color system revealed that L* is influenced only by illuminance, while a* and b* are affected solely by color temperature, and the correlations were high. This is attributed to the fact that L* represents the brightness of the color, while a* and b* are indicators of hue. Based on the analysis of the relationship between L* and illuminance, as well as a* and b* with color temperature within the CIELAB color system, I proposed the following soil color correction equations according to varying lighting conditions. In this context, I and T represent the illuminance and Color temperature received by the soil, respectively. aL and fL denote the slope and intercept of the linear regression equation between the L* value of soil color and Illuminance, while aa and fa represent the slope and intercept of the linear regression equation between the a* value of soil color and color temperature, and ab and fb signify the slope and intercept of the linear regression equation between the b* value of soil color and color temperature. For dry soil, it was confirmed that the slopes (i.e., aL, aa, ab) in Equations (1) to (3) are similar, regardless of the type of sample. Ultimately, the following correction equation was proposed. Through the proposed method, it is possible to correct the soil color of soil samples captured under arbitrary lighting conditions to the desired soil color under specific lighting conditions. More detailed correction procedures are described in Baek et al. (2023). Epilogue The KICT is currently developing a digital image-based soil color analysis technology that can consider irregular lighting conditions in the field. As shown by the results of an analysis of captured images, it appears that the impact of irregular lighting conditions on soil color can be eliminated (or corrected) based on the CIELAB color system. Using the analysis results for dry soil samples, a lighting condition correction equation has been proposed. In addition, currently, analyses are being conducted for soil samples containing water. Once the analysis for water-containing unsaturated soils is completed, it will become possible to acquire soil color quickly and easily from digitally captured soil images in the field, regardless of moisture content, enabling statistical analysis. Department of Geotechnical Engineering Research Date2023-10-11 Hit 678

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