Structural Identification Using Computer Vision-Based Bridge Health Monitoring

被引:88
|
作者
Khuc, Tung [1 ,2 ]
Catbas, F. Necati [3 ,4 ]
机构
[1] Natl Univ Civil Engn, Dept Bridge & Highways Engn, 55 Giai Phong St, Hanoi 100000, Vietnam
[2] Univ Cent Florida, 4000 Cent Florida Blvd, Orlando, FL 32816 USA
[3] Univ Cent Florida, Dept Civil Environm & Construct Engn, 4000 Cent Florida Blvd, Orlando, FL 32816 USA
[4] Bogazici Univ, TR-34342 Istanbul, Turkey
基金
美国国家科学基金会;
关键词
VEHICLE DETECTION; SYSTEM-IDENTIFICATION; INFLUENCE LINES; DISPLACEMENT; CLASSIFICATION; TRACKING; SENSOR;
D O I
10.1061/(ASCE)ST.1943-541X.0001925
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper presents a new structural identification (St-Id) framework along with a damage indicator, displacement unit influence surface using computer vision-based measurements for bridge health monitoring. Unit influence surface (UIS) of a certain response (e.g.,displacement, strain) at a measurement location on a beam-type or plate-type structure (e.g.,single-span or multispan bridge with its deck) is defined as a response function of the unit load with respect to the any given location of the unit load on that structure. The novel aspect of this paper is a framework integrating vehicle load (input) modeling using computer vision and the development of a new damage indicator, UIS, using image-based structural identification. This framework is demonstrated on the large-scale bridge model in the University of Central Florida Structures Laboratory for verification and validation. The UIS damage indicators successfully identified the simulated damage on the bridge model, including damage detection and damage localization.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Vision-based structural scaling factor and flexibility identification through mobile impact testing
    Tian, Yongding
    Zhang, Jian
    Yu, Shanshan
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 122 : 387 - 402
  • [42] Computer vision-based recognition of driver distraction: A review
    Moslemi, Negar
    Soryani, Mohsen
    Azmi, Reza
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2021, 33 (24)
  • [43] Computer Vision-Based Human Comfort Assessment of Stadiums
    Celik, Ozan
    Dong, Chuan-Zhi
    Catbas, F. Necati
    JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES, 2020, 34 (02)
  • [44] Computer vision-based interior construction progress monitoring: A literature review and future research directions
    Ekanayake, Biyanka
    Wong, Johnny Kwok-Wai
    Fini, Alireza Ahmadian Fard
    Smith, Peter
    AUTOMATION IN CONSTRUCTION, 2021, 127
  • [45] Marker-free monitoring of the grandstand structures and modal identification using computer vision methods
    Dong, Chuan-Zhi
    Celik, Ozan
    Catbas, F. Necati
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2019, 18 (5-6): : 1491 - 1509
  • [46] Computer vision-based plants phenotyping: A comprehensive survey
    Meraj, Talha
    Sharif, Muhammad Imran
    Raza, Mudassar
    Alabrah, Amerah
    Kadry, Seifedine
    Gandomi, Amir H.
    ISCIENCE, 2024, 27 (01)
  • [47] Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques
    Erfani, Seyed Mohammad Hassan
    Goharian, Erfan
    JOURNAL OF HYDROINFORMATICS, 2023, 25 (03) : 835 - 850
  • [48] Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles
    Hoskere, Vedhus
    Park, Jong-Woong
    Yoon, Hyungchul
    Spencer, Billie F., Jr.
    JOURNAL OF STRUCTURAL ENGINEERING, 2019, 145 (07)
  • [49] Bridge influence surface identification using a deep multilayer perceptron and computer vision techniques
    Jian, Xudong
    Xia, Ye
    Chatzi, Eleni
    Lai, Zhilu
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2024, 23 (03): : 1606 - 1626
  • [50] Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review
    Hwang, Sung-Wook
    Sugiyama, Junji
    PLANT METHODS, 2021, 17 (01)