• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.4
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• pp.62-69
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• 2023

This study investigated the electromechanical properties of cement based smart repair materials (SRMs) according to the different amounts of fine steel slag aggregates (FSSAs). SRMs can self-diagnose the quality of repairing and self-sense the damage of repaired zone. The replacement ratios of FSSAs to sand for SRMs were 0% (FSSA00), 25% (FSSA25), and 50% (FSSA50) by sand weight. The electrical resistivity of SRMs generally decreased as the compressive stress of SRMs increased: the electrical resistivity of FSSA25 at the age of 7 hours decreased from 78.16 to 63.68 kΩ-cm as the compressive stress increased from 0 to 22.37 MPa. As the replacement ratio of FSSAs by weight of sand increased from 0% to 25%, the stress sensitivity coefficient (SSC) of SRM at the age of 7 h increased from 0.471 to 0.828 %/MPa owing to the increased number of partially conductive paths in the SRMs. However, as the replacement ratio of FSSAs further increased up to 50%, the SSC decreased from 0.828 to 0.649 %/MPa because some of the partially conductive paths changed to continued conductive ones. SRMs are expected to self-sense the quality and future damage of repaired zone only by measuring the electrical resistivity of the repaired zone in addition to fast recovery in the mechanical resistance of structures.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.425-428
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• 2011

국내외적으로 수주량이 증가하고 있는 대형 구조물의 건설 시 보다 정밀한 시공 및 유지관리 기술이 요구된다. 그 중 콘크리트의 강도는 대표적인 설계변수 중 하나로 정확한 강도 값의 측정 및 이력관리는 건설 프로세스에서의 비용절감과 효율적인 시공관리를 위해 매우 중요한 요구사항이다. 이에 본 논문에서는 최근 개발된 임베디드 자율감지형 콘크리트 강도 모니터링 기술을 유비쿼터스 시대에 적합한 건설 기술로의 향상을 위해 QR코드와 연동시킨 강도 라벨링을 개발하고 이를 통하여 콘크리트의 강도이력 DB를 언제 어디서나 실시간으로 확인 및 관리할 수 있는 콘크리트 Life-Cycle 품질관리 시스템을 제안한다.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.235-242
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• 2022

The percolation threshold of the CNT-reinforced cement paste is closely related to the optimal CNT amount to maximize the sensing ability of self-sensing concrete. However, the percolation threshold has various values depending on the cement, CNT, and water-to-cement ratio used. In this study, a percolation simulation model was proposed to predict the percolation threshold of the CNT-reinforced cement paste. The proposed model can simulate the percolation according to the amount of CNT using only the properties of CNT and cement, and for this, the concept of the number of aggregated CNT particles was used. The percolation simulation consists of forming a pre-hydrated cement paste model, random dispersion of CNTs, and percolation investigation. The simulation used CNT-reinforced cement paste with a water-cement ratio of 0.4 to 0.6, and the simulated percolation threshold point showed high accuracy with a simulation residual ratio of up to 7.5 % compared to the literature results.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.