Deep learning enhanced principal component analysis for structural health monitoring. (July 2022)

• Record Type: Journal Article
• Authors: Fernandez-Navamuel, Ana; Magalhães, Filipe; Zamora-Sánchez, Diego; Omella, Ángel J; Garcia-Sanchez, David; Pardo, David
• Is Part Of: Structural health monitoring. Volume 21:Number 4(2022)
• Page Start: 1710
• Page End: 1722
• Publication Date: 2022-07
• Subjects: Structural Health Monitoring -- Deep Learning -- Principal Component Analysis -- autoencoder -- reconstruction error; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217211041684
• Languages: English
• ISSNs: 1475-9217

Abstract

This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.