Supervised and Unsupervised Machine Learning Approaches for Bridge Damage Prediction

Structural health monitoring (SHM) starts to gain increasing attentions in the recent decade with the enormous support received from information and communication technology (ICT) which provides not only efficient data acquisition and transmission instruments, but also data analysis techniques for system modeling. In this work, we present a novel approach for bridge health assessment and damage localization based on vibration monitoring. Specifically, we consider one of the largest bridges in Sydney as a study case. We aim to distinguish damaged and undamaged joints in the bridge via supervised and unsupervised machine learning methods. Health conditions of bridge joints are reflected by their vibrations caused by vehicle passing events. The proposed machine learning methods train classifiers on historical vibration data for the purpose of distinguishing vibrations from healthy and unhealthy joints. Vibration events are recorded by 18 tri-axial accelerometers, 3 per joint with a total of 6 joints. Attempts have been made to normalize input data by selecting events with the dominant occurrence order of vibration peaks. Both time domain and frequency domain features have been used for training and testing. 10-fold cross validation is used for evaluation. For supervised method, 99.71% and 99.30% detection accuracies are achieved for time and frequency domain features respectively. Peak selection boosts the detection accuracy from 69% to 84% for the unsupervised method.