Journal Article

Unsupervised machine and deep learning methods for structural damage detection: A comparative study

Wang, Zilong, Cha, Young‐Jin

2025

Overview

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 article 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 article is that only normal data from undamaged/baseline 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. Recently, deep learning‐based damage detection is a very hot topic. This article conducted extensive comparative studies using state‐of‐the‐art methods of deep learning‐based damage detection methods to figure out the pros and cons of each method.

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Clustering / Comparative studies / Damage detection / Data acquisition / Datasets / Deep learning / fast clustering / Literature reviews / Machine learning / Probabilistic models / Sensors / Statistical methods / Statistical models / Steel bridges / structural damage detection / Support vector machines / Unsupervised learning / unsupervised novelty detection