Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detect...Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.展开更多
The impact of earthquakes in urban centers prone to disastrous earthquakes necessitates the analysis of associ- ated risk for rational formulation of contingency plans and mitigation strategies.In urban centers,the se...The impact of earthquakes in urban centers prone to disastrous earthquakes necessitates the analysis of associ- ated risk for rational formulation of contingency plans and mitigation strategies.In urban centers,the seismic risk is best quantified and portrayed through the preparation of'Earthquake Damage and Loss Scenarios.'The components of such scenarios are the assessment of the hazard,inventories and the vulnerabilities of elements at risk.For the development of the earthquake risk scenario in Istanbul,two independent approaches,one based on intensities and the second on spectral displacements,are utilized.This paper will present the important features of a comprehensive study,highlight the method- ology,discuss the results and provide insights to future developments.展开更多
基金National Key R&D Program of China under Grant No.2017YFC1500606,National Natural Science Foundation of China under Grant No.52020105002Heilongjiang Touyan Innovation Team Program。
文摘Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.
文摘The impact of earthquakes in urban centers prone to disastrous earthquakes necessitates the analysis of associ- ated risk for rational formulation of contingency plans and mitigation strategies.In urban centers,the seismic risk is best quantified and portrayed through the preparation of'Earthquake Damage and Loss Scenarios.'The components of such scenarios are the assessment of the hazard,inventories and the vulnerabilities of elements at risk.For the development of the earthquake risk scenario in Istanbul,two independent approaches,one based on intensities and the second on spectral displacements,are utilized.This paper will present the important features of a comprehensive study,highlight the method- ology,discuss the results and provide insights to future developments.
