Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: A systematic review

Abstract

Damage detection and safety assessment play a prominent role in the integrity management of bridge structures. Environmental and operational variability are the leading factors that cause the deterioration of bridge structures. The extensive adoption of vibration-based monitoring techniques and machine learning involved interaction development among multi-disciplines which bringing a rapid digital transformation in maintaining the continuous performance of existing bridges with the help of big data. To sustain and preserve the bridge structure during its lifetime, it is essential to conduct early monitoring. Therefore, through various critical literature, this brief review aims to investigate the latest progress, drawbacks, and future trends in utilizing vibration-based condition monitoring and machine learning techniques. These approaches offer advantages to handle complex problems by providing computational efficiency, treating uncertainties, and facilitating the decision-making process. This study offers fruitful perspectives and suggestions for practicing vibration-based damage detection and machine learning techniques in bridge health monitoring.

Keywords

• Bridge monitoring
• Machine learning
• Structural health monitoring
• Vibration-based monitoring
• Bridge damage detection