Scalable bridge health monitoring using drive-by vehicles: PhD forum abstract

Abstract

The objective of my thesis work is to develop physics-guided data-driven approaches for drive-by bridge health monitoring (BHM) that are scalable and eliminate the need of acquiring training data from every bridge. BHM allows us to detect bridge damage in earlier stages, which is essential for preventing more severe damage and collapses that may lead to significant business and human losses. Using vibrations from drive-by vehicles for BHM has various advantages, such as: economical and no need for on-site maintenance of equipment on bridges. However, many such approaches face analysis challenges for monitoring multiple bridges because 1) they either require labeled data from each bridge, which is expensive and time-consuming to collect, or 2) if we directly apply the supervised model trained for one bridge to other bridges, damage diagnostic accuracy could significantly reduce because of distribution mismatch between different bridges' data. In this work, I overcome the first challenge by leveraging physical insights about the vehicle-bridge interaction (VBI) to guide the data-driven approach and diagnose damage in a semi-supervised way. Also, I overcome the second challenge through a domain adversarial training and multi-task learning framework. The framework extracts features that are sensitive to damages and invariant across bridges to monitor multiple bridges without the need for training data from every bridge.