Research Information

Development of Smart Monitoring System for Concrete Structures Using FRP Nerve Sensors
  • Date2021-06-29
  • Hit73

Development of Smart Monitoring System for Concrete Structures Using FRP Nerve Sensors

 

 

▲ Senior Research Fellow Park Young-hwan, Department of Infrastructure Safety Research

 

 

Foreword


As of December 31, 2019, out of the total of 35,902 highway bridges in Korea, 81.7% were concrete bridges, while all 2,682 of the highway tunnels were concrete structures. Most of today's social overhead capital (SOC) infrastructures are concrete structures. Concrete structures deteriorate due to a range of factors, and their structural safety degrades due to excessive loads. But up to now, the safety management and maintenance of concrete structures has mainly depended on visual inspection by manpower, and thus remains at a subjective and qualitative level. Accordingly, there are problems in terms of reliability, real-time status identification, and response to safety assessment results. To solve this problem, in this study, we developed embedded distributed fiber optic sensors for concrete structures (Nerve Sensors in Concrete Structures), which have excellent durability and reliability and address the problems of existing point-type sensors, and installed them in concrete structures, enabling them to measure the strain of concrete structures in many locations in a manner similar to the human body's nerves. Through their application, we developed a technology that enables the smart safety management of concrete structures.

 

 

 

Development of Nerve Sensor


The nerve sensors in concrete structures developed in this study are based on optical fibers with excellent durability and reliability. As optical fiber is vulnerable to damage, it is difficult to embed it in a concrete structure. For this reason, we devised a method to protect the optical fiber by embedding it in a circular FRP (Fiber Reinforced Polymer) rod, and developed a manufacturing technology to produce it efficiently. To this end, the use of pultrusion and braidtrusion were reviewed, and manufacturing technology that employs pultrusion was adopted in consideration of quality control. For nerve sensors to properly grasp the behavior of concrete members, integrity with the concrete must be secured. To accomplish this, the study was conducted on the surface shape of nerve sensor and surface treatment technology. To connect the nerve sensors to the measuring instrument after embedding them in the concrete structure, the optical fiber must be removed from the FRP rod. Since the FRP rod is made of thermosetting resin, it is not easy to abstract the optical fiber. Therefore, it is necessary to develop technology for this. In this study, heat abstraction, mechanical abstraction, and chemical abstraction were studied and reviewed, and an optimal technology for easy optical fiber abstraction was developed.


To measure the strain of the structure at many locations with nerve sensors, optical fiber-based distributed measurement technology is needed. The distributed measurement technology developed thus far is not suitable for monitoring concrete structures in terms of the measurement interval, measurement accuracy, measurement time, etc. In this study, a distributed measuring instrument that solves these problems was developed in collaboration with specialist organizations, and its performance was verified through a number of experiments. However, the developed distributed measuring instrument has a problem in that dynamic measurement is not easy. To solve this problem, a semi-distributed dynamic measuring instrument was also studied, and the applicability was confirmed through various verifications of the developed prototype (Figure 2).

 

 

 

Development of Nerve Sensor Utilization Technology


Since the purpose of developing the nerve sensor is to directly measure the strain occurring in the concrete member, it is essential to ensure the reliability of the measurement in order to secure an integrated behavior between the concrete and the nerve sensor. In other words, between the surface of the FRP rod constituting the nerve sensor and the concrete surrounding it, it is necessary to secure an adhesive force that is greater than the required performance to ensure the reliability of the measurement. In this study, various surface treatments were examined for this purpose, and suitable surface treatment methods were derived through adhesion experiments. By comparing the value measured by the nerve sensor after installing the developed nerve sensor in the concrete member with the value measured by the existing verified sensor (electrical resistance strain sensor, FBG-based fiber optic sensor), it was confirmed that the nerve sensor has sufficient accuracy for structure monitoring. The values were obtained during the loading test. The method of installing the nerve sensors in new structures and in existing structures is different. In a new structure, the nerve sensor is installed along the rebar, so it can be placed relatively easily. But in an existing structure, a groove must be created on the concrete surface and the nerve sensor embedded using adhesive (Figure 3).

 

 


Since the nerve sensor uses optical fiber, it has the advantage of remotely acquiring data using an existing optical (photonic) network. In other words, it is possible to connect the nerve sensors embedded in multiple structures to the optical network, and measure each nerve sensor using the optical instrument at the base station or the integrated control office. In this way, the safety of multiple structures can be efficiently managed (Figure 4).

 

 

 

Development of Smart Monitoring Technology Based on Digital Twin


Digital twin technology was used to detect, evaluate, and predict damage to structures using data collected from nerve sensors installed in structures. A digital twin is a computer model (digital model) of an existing structure. By reflecting the sensor data in the digital twin, it is possible to understand or predict the current condition of the real structure by computer-simulating situations that may occur in the real structure. As a result, the conditions and safety of the structure can be efficiently managed (Figure 5).


To assess the performance of a structure after it has been built using a digital twin, a finite element analysis program is needed. The existing programs in this area that are commercially available cannot implement nerve sensors. They also have high prices and license issues. For this reason, we developed our own analysis engine in this study. When a digital twin is built using acceleration data, changes in the overall behavior of the structure can be detected, but localized damage is difficult to detect. However, if a digital twin is built using the strain data of the nerve sensors, it has the advantage not only of detecting the overall behavior change but also of detecting important local damage in terms of maintenance. In this study, a strain-based model updating technology was developed to reflect the characteristics of the nerve sensors, and an integrated digital twin system was developed to assess the damage and performance of the structure by linking it with its own analysis engine (Figure 6). The applicability of the developed technology was verified by comparing the results of the integrated digital twin system that does not require a load test with the results of the existing method to assess the performance of a structure through an in-situ load test for an actual bridge to which the nerve sensors are applied.


 

 

 

Conclusion


In this study, a measuring instrument related to a fiber optic-based nerve sensor was developed to scientifically and efficiently manage the condition of concrete structures, and its performance was verified through various experiments. To assess the safety and damage of structures, a digital twin technique using nerve sensor data was developed, and its potential was verified through its application to the field. The technology developed in this study can be applied to both new and existing concrete structures, and is expected to contribute to the smart monitoring of concrete structures in the future.

 

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