Jing Zhou, Linsheng Huo, Chen Huang, Zhuodong Yang, Hongnan Li
{"title":"利用监控摄像机图像评估地震对建筑物造成破坏的可行性研究","authors":"Jing Zhou, Linsheng Huo, Chen Huang, Zhuodong Yang, Hongnan Li","doi":"10.1155/2024/4993972","DOIUrl":null,"url":null,"abstract":"<div>\n <p>Rapid and accurate structural damage assessment after an earthquake is important for efficient emergency management. The widespread application of surveillance cameras provides a new possibility for improving the efficiency of assessment. However, it is still challenging to directly assess the structural seismic damage based on videos captured by indoor surveillance cameras during earthquakes. In this study, we elaborate on the concept of estimating the structural natural frequency based on the relative pixel displacement of inter-stories. Furthermore, we propose a strategy for post-earthquake structural damage assessment that integrates the computer vision and time-frequency analysis. This approach aims to navigate the difficulties inherent in earthquake damage assessment and improve emergency responses. The relative pixel displacement between the camera and the fixed features on the floor is extracted from videos by using the Harris corner detection and Kanade–Lucas–Tomasi algorithms. The structural natural frequency is estimated using the synchroextracting transform-enhanced empirical wavelet transform. The natural frequency shift-related seismic damage index is defined and calculated for damage assessment. A shake table experiment of a small-scale steel model is conducted to verify the accuracy and feasibility of the approach, and the practicality of the proposed approach is further verified by utilizing the data from a full-scale reinforced concrete benchmark model experiment. The results demonstrate that the approach can accurately and efficiently evaluate the structural damage after an earthquake based on the video captured by surveillance cameras during the earthquake. The error of the acquired damage index is less than 0.1. We will apply more advanced algorithms in the future to alleviate this problem.</p>\n </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4993972","citationCount":"0","resultStr":"{\"title\":\"Feasibility Study of Earthquake-Induced Damage Assessment for Structures by Utilizing Images from Surveillance Cameras\",\"authors\":\"Jing Zhou, Linsheng Huo, Chen Huang, Zhuodong Yang, Hongnan Li\",\"doi\":\"10.1155/2024/4993972\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>Rapid and accurate structural damage assessment after an earthquake is important for efficient emergency management. The widespread application of surveillance cameras provides a new possibility for improving the efficiency of assessment. However, it is still challenging to directly assess the structural seismic damage based on videos captured by indoor surveillance cameras during earthquakes. In this study, we elaborate on the concept of estimating the structural natural frequency based on the relative pixel displacement of inter-stories. Furthermore, we propose a strategy for post-earthquake structural damage assessment that integrates the computer vision and time-frequency analysis. This approach aims to navigate the difficulties inherent in earthquake damage assessment and improve emergency responses. The relative pixel displacement between the camera and the fixed features on the floor is extracted from videos by using the Harris corner detection and Kanade–Lucas–Tomasi algorithms. The structural natural frequency is estimated using the synchroextracting transform-enhanced empirical wavelet transform. The natural frequency shift-related seismic damage index is defined and calculated for damage assessment. A shake table experiment of a small-scale steel model is conducted to verify the accuracy and feasibility of the approach, and the practicality of the proposed approach is further verified by utilizing the data from a full-scale reinforced concrete benchmark model experiment. The results demonstrate that the approach can accurately and efficiently evaluate the structural damage after an earthquake based on the video captured by surveillance cameras during the earthquake. The error of the acquired damage index is less than 0.1. We will apply more advanced algorithms in the future to alleviate this problem.</p>\\n </div>\",\"PeriodicalId\":49471,\"journal\":{\"name\":\"Structural Control & Health Monitoring\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4993972\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Control & Health Monitoring\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/4993972\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Control & Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/4993972","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Feasibility Study of Earthquake-Induced Damage Assessment for Structures by Utilizing Images from Surveillance Cameras
Rapid and accurate structural damage assessment after an earthquake is important for efficient emergency management. The widespread application of surveillance cameras provides a new possibility for improving the efficiency of assessment. However, it is still challenging to directly assess the structural seismic damage based on videos captured by indoor surveillance cameras during earthquakes. In this study, we elaborate on the concept of estimating the structural natural frequency based on the relative pixel displacement of inter-stories. Furthermore, we propose a strategy for post-earthquake structural damage assessment that integrates the computer vision and time-frequency analysis. This approach aims to navigate the difficulties inherent in earthquake damage assessment and improve emergency responses. The relative pixel displacement between the camera and the fixed features on the floor is extracted from videos by using the Harris corner detection and Kanade–Lucas–Tomasi algorithms. The structural natural frequency is estimated using the synchroextracting transform-enhanced empirical wavelet transform. The natural frequency shift-related seismic damage index is defined and calculated for damage assessment. A shake table experiment of a small-scale steel model is conducted to verify the accuracy and feasibility of the approach, and the practicality of the proposed approach is further verified by utilizing the data from a full-scale reinforced concrete benchmark model experiment. The results demonstrate that the approach can accurately and efficiently evaluate the structural damage after an earthquake based on the video captured by surveillance cameras during the earthquake. The error of the acquired damage index is less than 0.1. We will apply more advanced algorithms in the future to alleviate this problem.
期刊介绍:
The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications.
Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics.
Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.