Vehicle assisted bridge damage assessment using probabilistic deep learning

Abstract

Vehicle assisted monitoring has shown promising potential for the condition assessment of existing bridges in a road network, by removing practical complications faced in traditional Structural health monitoring (SHM) methods such as traffic interruption and dense deployment of sensors. However, the combination of different measurement sources during vehicle assisted monitoring has not yet been fully explored. This paper aims to evaluate the potential benefit of considering multiple measured responses from various sources, including fixed sensors on the bridge and on-board vehicle sensors. To this end, this paper proposes a Probabilistic Deep Neural Network, a stochastic data-driven framework for damage assessment. This framework enables the combination of vehicle and bridge responses to extract damage sensitive features for the classification of different damage states. In addition, the proposed method estimates the uncertainty of its predictions, providing an indication of the reliability of the result. The proposed method is validated using two numerical based case studies while considering realistic operational conditions, which include temperature oscillations, additional traffic, and measurement noise. The results from this study indicate that combining multiple sensor information results in lower uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.