Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization

AbstractDamage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the ex...

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Bibliographic Details
Published in:Journal of computing in civil engineering 2019-05, Vol.33 (3)
Main Authors: Gulgec, Nur Sila, Takáč, Martin, Pakzad, Shamim N
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer simulation
Computing time
Damage assessment
Damage detection
Damage localization
Damage patterns
Data processing
Diagnosis
Finite element method
Image detection
Inverse problems
Localization
Model testing
Neural networks
Object recognition
Position (location)
Robustness (mathematics)
Sensor arrays
Structural damage
Structural health monitoring
Technical Papers
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Summary:AbstractDamage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the existence, location, and severity of damage. Therefore, this procedure requires complex data processing algorithms and dense sensor arrays, which brings computational intensity with it. To address this limitation, this paper introduces convolutional neural network (CNN), which is one of the major breakthroughs in image recognition, to the damage detection and localization problem. The CNN technique has the ability to discover abstract features and complex classifier boundaries that are able to distinguish various attributes of the problem. In this paper, a CNN topology was designed to classify simulated damaged and healthy cases and localize the damage when it exists. The performance of the proposed technique was evaluated through the finite-element simulations of undamaged and damaged structural connections. Samples were trained by using strain distributions as a consequence of various loads with several different crack scenarios. Completely new damage setups were introduced to the model during the testing process. Based on the findings of the proposed study, the damage diagnosis and localization were achieved with high accuracy, robustness, and computational efficiency.
ISSN:0887-3801
1943-5487
DOI:10.1061/(ASCE)CP.1943-5487.0000820