A deep-learning framework for classifying the type, location, and severity of bridge damage using drive-by measurements

Abstract

This paper proposes a new deep-learning framework for drive-by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre-measured training data. The computational framework adopts a numerical vehicle–bridge interaction (VBI) model, which is automatically calibrated using only the vehicle vibration measurements. The calibrated model is used to generate labeled training data, eliminating the practical difficulties associated with collecting training data for damaged bridge scenarios. The numerically simulated data are used to train a convolutional neural network that can then classify the actual damage characteristics of a bridge. The method is tested using a laboratory-scale VBI model, and results show that the algorithm can accurately identify the presence of seized bearings and cracking in the bridge beams. The algorithm shows good accuracy when identifying the type and extent of damage. The location of bearing damage can generally be identified; however, the location of cracking is less accurate at lower damage levels. The proposed framework represents a significant improvement on existing techniques for indirect bridge monitoring.