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Damage Detection for Civil Structural Health Monitoring Application - A Case Study of the Steel Grid Bridge Structural Model

Authors :
Zoran Mišković
Ahmed Alalikhan
Saad Al-Wazni
Source :
Tehnički Vjesnik, Vol 25, Iss Supplement 2, Pp 266-275 (2018), Tehnički vjesnik, Volume 25, Issue Supplement 2, Tehnički vjesnik / Technical Gazette
Publication Year :
2018
Publisher :
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek, 2018.

Abstract

The procedure for detecting the location and severity of damage of complex structural systems using their modal properties is an important tool of Structural Health Monitoring (SHM) of civil infrastructure. The herein presented research proposes procedures for damage detection based on two heuristic optimization methods: Simulated Annealing (SA) and Tabu Search (TS). In order to test the proposed procedures in different frequency ranges, experimental and numerical analyses were conducted on a steel grid bridge model in two configurations, according to the total mass of the structure, as well as for two simulated damage cases. The calibration of model parameters, according to experimentally extracted modal properties, is carried out using the proposed procedures. Numerical computations were conducted using ANSYS package and developed routines under MATLAB environment for model calibration and damage detection procedures. Experimental modal properties were extracted from ambient vibration measurements, as state-of- the art in SHM of complex structures, by the Frequency Domain Decomposition (FDD) technique, using ARTeMIS software. Both of the proposed procedures for model calibration and damage detection, with adopted objective functions including frequency and mode shape differences, exhibit accuracy, efficiency and robustness.

Details

Language :
English
ISSN :
18486339 and 13303651
Volume :
25
Database :
OpenAIRE
Journal :
Tehnički Vjesnik
Accession number :
edsair.doi.dedup.....b163d534bb71c72eece44ee341f3c257