Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

• Published in: Applied sciences Vol. 10; no. 8; p. 2786
• Main Authors: Shokravi, Hoofar, Shokravi, Hooman, Bakhary, Norhisham, Rahimian Koloor, Seyed Saeid, Petrů, Michal
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Algorithms; Bridges; Civil engineering; Defects; Interdisciplinary subjects; Kalman filters; Methods; Parameter identification; Smart cities; Strain gauges; structural health monitoring (SHM); subspace system identification (SSI); system identification; Vehicles; vibration-based damage detection
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10082786

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.