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Unsupervised Machine and Deep Learning Methods for Structural Damage Detection: A Comparative Study
  • Zilong Wang,
  • Young-Jin Cha
Zilong Wang
Suzhou Institute of Building Science Group

Corresponding Author:[email protected]

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Young-Jin Cha
University of Manitoba Faculty of Engineering
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While many structural damage detection methods have been developed in recent decades, few data-driven methods in unsupervised learning mode have been developed to solve the practical difficulties in data acquisition for civil infrastructures in different scenarios. To address such a challenge, this paper proposes a number of improved unsupervised novelty detection methods and conducts extensive comparative studies on a laboratory scale steel bridge to examine their performances of damage detection. The key concept behind unsupervised novelty detection in this paper is that only normal data from undamaged structural scenarios are required to train statistical models with these methods. Then, these trained models are used to identify abnormal testing data from damaged scenarios. To detect structural damage in the form of loosening bolts in the steel bridge, four machine-learning methods (i.e., K-nearest neighbors method, Gaussian mixture models, One-class support vector machines, Density peaks-based fast clustering method) and one deep learning method using a deep auto-encoder are selected. Meanwhile, some modifications and improvements are made to enable these methods to detect structural damage in unsupervised novelty detection mode. In their comparative studies, the advantages and disadvantages of these methods are analyzed based on their results of structural damage detection.
21 Feb 2022Submitted to Engineering Reports
22 Feb 2022Submission Checks Completed
22 Feb 2022Assigned to Editor
25 Feb 2022Reviewer(s) Assigned
31 Mar 2022Editorial Decision: Revise Major
20 May 20221st Revision Received
24 May 2022Submission Checks Completed
24 May 2022Assigned to Editor
24 May 2022Reviewer(s) Assigned
23 Jun 2022Editorial Decision: Revise Minor
23 Jun 20222nd Revision Received
24 Jun 2022Submission Checks Completed
24 Jun 2022Assigned to Editor
24 Jun 2022Editorial Decision: Accept