Time domain damage localization and quantification in seismically excited structures using a limited number of sensors

This paper presents a new algorithm to determine the occurrence, location, and severity of damage in structures subjected to earthquakes. The algorithm is based on the analysis of the time series associated with displacement or acceleration, and provided by a limited number of sensors. The algorithm...

Full description

Saved in:
Bibliographic Details
Published in:Journal of vibration and control 2017-10, Vol.23 (18), p.2942-2961
Main Authors: Bagheri, Abdollah, Zare Hosseinzadeh, Ali, Rizzo, Piervincenzo, Ghodrati Amiri, Gholamreza
Format: Article
Language:English
Subjects:
Algorithms
Business competition
Computer simulation
Damage detection
Damage localization
Discrete Wavelet Transform
Earthquake damage
Earthquakes
Evolutionary algorithms
Mathematical models
Noise
Objective function
Optimization
Robustness (mathematics)
Seismic activity
Sensors
Structural damage
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new algorithm to determine the occurrence, location, and severity of damage in structures subjected to earthquakes. The algorithm is based on the analysis of the time series associated with displacement or acceleration, and provided by a limited number of sensors. The algorithm is formulated in terms of an optimization problem. An objective function is defined based on the moment generating function for a segment of the time histories and an evolutionary optimization strategy, based on the competitive optimization algorithm, is employed to detect damage. The efficiency of the proposed method is numerically validated by studying the response of some structures subjected to the 1940 El-Centro earthquake and the 1994 Northridge earthquake. In order to simulate real conditions, different levels of noise are added to the response’s signals, and then the discrete wavelet transform is used to de-noise the signals. Moreover, the robustness of the method is evaluated by considering an error in the model of the structures. Overall, we find that the proposed algorithm detects and localizes damage even in presence of noisy signals and errors in the model.
ISSN:1077-5463
1741-2986
DOI:10.1177/1077546315625141