Application of supervised learning to validation of damage detection
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O R I G I NA L
Hassan Sarmadi
· Alireza Entezami
Application of supervised learning to validation of damage detection
Received: 15 March 2020 / Accepted: 7 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Unsupervised learning methods are effective and suitable tools for damage detection. The main reason for the popularity of these methods in structural health monitoring originates from the fact that the process of learning can be implemented by information of the only normal condition called training data. In contrast, supervised learning methods require information of both normal and current conditions for the process of interest. Because civil engineering structures are expensive and complex, it is not reasonable and economical to impose intentional damage on providing training data. Hence, it is not simple to directly exploit supervised learning techniques in structural health monitoring. To deal with this limitation, this article proposes a novel two-level strategy including three algorithms for using the concepts of both unsupervised learning and supervised learning. The major contribution of this strategy is to consider supervised learning as a validation tool for damage detection. First, the results of damage detection are obtained from two unsupervised learning methods developed by Mahalanobis squared distance and a deep autoencoder neural network in the first two algorithms of the proposed strategy. The main objective is to separate accurate and confusing results of damage detection based on Type I and Type II errors. Second, the confusing results are fed into the third algorithm to train a classifier and compute their classification margins for making the final decision and validating damage detection. The effectiveness and applicability of the proposed strategy are assessed by a numerical concrete beam and an experimental laboratory frame. Results show that this strategy with the aid of the Naïve Bayes classifier enables the unsupervised learning methods to make accurate decisions. Keywords Structural health monitoring · Damage detection · Unsupervised learning · Novelty detection · Supervised learning · Classification 1 Introduction Detection of damage is an important issue in most of the civil engineering structures. Structural health monitoring (SHM) is a nondestructive process that aims to implement practical approaches to assessing current conditions of structural systems and detecting any damage by using vibration data [1–5], lamb waves [6], H. Sarmadi (B) · A. Entezami Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad P.Box: 9177948974,, Iran E-mail: [email protected] H. Sarmadi Head of Research and Development, Ideh Pardazan Etebar Sazeh Fanavar Pooya (IPESFP) Company, 29th Reza St., Reza Blvd, Mashhad, Razavi Khorasan P.Box: 9176768544, Iran E-mail: [email protected] A. Entezami Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milan, Italy
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