Concrete bridge damage detection using parallel simulation

被引:8
作者
Lin, Fangzheng [1 ]
Scherer, Raimar J. [1 ]
机构
[1] Tech Univ Dresden, Inst Construct Informat, D-01069 Dresden, Germany
关键词
Bridge damage detection; Structural Health Monitoring; Concrete crack; Parallel simulation; Cloud computing; VIBRATION; IDENTIFICATION; LOCATION; ELEMENT; MODEL;
D O I
10.1016/j.autcon.2020.103283
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Bridge health assessment is an interdisciplinary area of high socio-economical value. Bridge damage detection technology is one of the most developed technologies today. This paper introduces a new simulation-based damage detection method for reinforced concrete bridge structures using parallel processing. The proposed method takes advantage of vehicle axle loads and high accuracy bridge response from an advanced bridge monitoring system, executes simultaneously variant structural analyses with hypothetical damage assumption to reduce processing duration and improve productivity of damage detection, and utilises the mathematical model of Residual Sum of Squares in the comparison of simulation results and monitoring data to determine the best-fit models. In the case study, seven scenarios are simulated to explore and verify the features of the proposed method. A discussion of perspectives for further implementations completes the paper.
引用
收藏
页数:14
相关论文
共 55 条
[1]   Damage detection in bridges using modal curvatures: Application to a real damage scenario [J].
Abdel Wahab, MM ;
De Roeck, G .
JOURNAL OF SOUND AND VIBRATION, 1999, 226 (02) :217-235
[2]   Internet of Things (IoT) Platform for Structure Health Monitoring [J].
Abdelgawad, Ahmed ;
Yelamarthi, Kumar .
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2017,
[3]   1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data [J].
Abdeljaber, Osama ;
Avci, Onur ;
Kiranyaz, Mustafa Serkan ;
Boashash, Boualem ;
Sodano, Henry ;
Inman, Daniel J. .
NEUROCOMPUTING, 2018, 275 :1308-1317
[4]   Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks [J].
Abdeljaber, Osama ;
Avci, Onur ;
Kiranyaz, Serkan ;
Gabbouj, Moncef ;
Inman, Daniel J. .
JOURNAL OF SOUND AND VIBRATION, 2017, 388 :154-170
[5]  
Andre J, 2000, ADV ENG SOFTW, V32, P49
[6]  
Baader F, 2003, IN HAND I S, P3
[7]  
Bezanson J., 2019, JULIA LANGUAGE
[8]   Damage detection in initially nonlinear systems [J].
Bornn, Luke ;
Farrar, Charles R. ;
Park, Gyuhae .
INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE, 2010, 48 (10) :909-920
[9]   Ambient vibration studies for system identification of tall buildings [J].
Brownjohn, JMW .
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS, 2003, 32 (01) :71-95
[10]   ONE-DIMENSIONAL THEORY OF CRACKED BERNOULLI-EULER BEAMS [J].
CHRISTIDES, S ;
BARR, ADS .
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, 1984, 26 (11-1) :639-648