Computer vision-based post-earthquake inspections for building safety assessment

被引:4
作者
Cheng, Min-Yuan [1 ]
Sholeh, Moh Nur [1 ]
Kwek, Alvin [1 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Civil & Construction Engn, 43,Sec 4,Keelung Rd, Taipei 106, Taiwan
来源
JOURNAL OF BUILDING ENGINEERING | 2024年 / 94卷
关键词
Building safety assessment; Computer vision; Damage recognition; Post-earthquake building inspections; Structural health monitoring; EARTHQUAKE;
D O I
10.1016/j.jobe.2024.109909
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Assessing the safety of earthquake-affected buildings is a critical structural health monitoring task that facilitates the timely response to present dangers and reduces potential threats to life and property. However, post-earthquake time constraints and harsh environmental conditions mean that images and videos taken on -site are frequently affected by poor resolution and blurriness, which may negatively affect the accuracy and usefulness of artificial intelligence image recognition tools. In this study, a HybridGAN model was developed that incorporates ESRGAN for resolution improvement and DeblurGANv2 for blurriness improvement. Additionally, a transfer learning U-Net (TF-Unet) was integrated to detect building components (i.e., columns and structural walls), classify building damage types, and identify building damage levels. Based on recognition results from three case studies and the relevant Taiwan codes, an automated system for building safety evaluation was proposed. The model was trained to directly classify and recognize the level of building component damage. The mean Intersection over Union (mIoU) results for the column and structural wall using the testing dataset were 81.326 % and 57.009 %, respectively. The pre-trained model was used to predict three case studies to test the capability of TF-Unet to handle real-word datasets.
引用
收藏
页数:22
相关论文
共 40 条
[1]   Computer vision framework for crack detection of civil infrastructure-A review [J].
Ai, Dihao ;
Jiang, Guiyuan J. ;
Lam, Siew-Kei ;
He, Peilan ;
Li, Chengwu .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 117
[2]   Remote Sensing of Urban Microclimate Change in L'Aquila City (Italy) after Post-Earthquake Depopulation in an Open Source GIS Environment [J].
Baiocchi, Valerio ;
Zottele, Fabio ;
Dominici, Donatella .
SENSORS, 2017, 17 (02)
[3]   Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach [J].
Carvalho, Osmar Luiz Ferreira de ;
de Carvalho Junior, Osmar Abilio ;
Albuquerque, Anesmar Olino de ;
Bem, Pablo Pozzobon de ;
Silva, Cristiano Rosa ;
Ferreira, Pedro Henrique Guimaraes ;
Moura, Rebeca dos Santos de ;
Gomes, Roberto Arnaldo Trancoso ;
Guimaraes, Renato Fontes ;
Borges, Dibio Leandro .
REMOTE SENSING, 2021, 13 (01) :1-24
[4]   Image quality enhancement using HybridGAN for automated railway track defect recognition [J].
Cheng, Min-Yuan ;
Khasani, Riqi Radian ;
Setiono, Kent .
AUTOMATION IN CONSTRUCTION, 2022, 146
[5]   Multi-agent-based data exchange platform for bridge disaster prevention: a case study in Taiwan [J].
Cheng, Min-Yuan ;
Wu, Yu-Wei .
NATURAL HAZARDS, 2013, 69 (01) :311-326
[6]   A Complementary Engineering-Based Building Damage Estimation for Earthquakes in Catastrophe Modeling [J].
Chian, Siau Chen .
INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE, 2016, 7 (01) :88-107
[7]   Post-earthquake preliminary seismic assessment method for low-rise RC buildings in Taiwan [J].
Chiu, Chien-Kuo ;
Sung, Hsin-Fang ;
Chiou, Tsung-Chih .
JOURNAL OF BUILDING ENGINEERING, 2022, 46
[8]   A comprehensive review of earthquake-induced building damage detection with remote sensing techniques [J].
Dong, Laigen ;
Shan, Jie .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2013, 84 :85-99
[9]   PEER Hub ImageNet: A Large-Scale Multiattribute Benchmark Data Set of Structure Images [J].
Gao, Yuqing ;
Mosalam, Khalid M. .
JOURNAL OF STRUCTURAL ENGINEERING, 2020, 146 (10)
[10]   A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications [J].
Gui, Jie ;
Sun, Zhenan ;
Wen, Yonggang ;
Tao, Dacheng ;
Ye, Jieping .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (04) :3313-3332