A review of bridge health monitoring based on machine learning

Abstract

This paper reviews structural health monitoring (SHM) techniques of bridge structures based on machine learning (ML) algorithms. Regular inspections and the use of non-destructive testing are still the common damage-detection methods; however, they are susceptible to subjectivity and human error and involve prolonged duration. With emerging technologies such as artificial intelligence and the development of wireless sensors, SHM has shifted from offline model-driven damage detection to online/real-time data-driven damage detection. In this paper, both supervised and unsupervised ML algorithms are examined to determine which of the latest methods would be the most suitable and effective for the SHM of bridge structures. This review paper investigates recent studies on data acquisition, data imputation, data compression, feature extraction and pattern recognition using supervised/unsupervised ML algorithms.