Doctor of Philosophy (PhD)
Department of Civil and Environmental Engineering
In the past few decades, the rapid growth of traffic volume and weight, and the aging of transportation infrastructures have raised serious concerns over transportation safety. Under these circumstances, vehicle overweight enforcement and bridge condition assessment through structural health monitoring (SHM) have become critical to the protection of the safety of the public and transportation infrastructures. The main objectives of this dissertation are to: (1) develop an enhanced bridge weigh-in-motion (BWIM) methodology that can be integrated into the SHM system for overweight enforcement and monitoring traffic loading; (2) present a Bayesian framework to predict the extreme traffic load effects (LEs) of bridges and assess the implication of the growing traffic on bridge safety.
Firstly, an enhanced BWIM methodology is developed. A comprehensive review on the BWIM technology is first presented. Then, a novel axle detection method using wavelet transformation of the bridge global response is proposed. Simulation results demonstrate that the proposed axle detection method can accurately identify vehicle axles, except for cases with rough road surface profiles or relatively high measurement noises. Furthermore, a two-dimensional nothing-on-road (NOR) BWIM algorithm that is able to identify the transverse position (TP) and axle weight of vehicles using only weighing sensors is proposed. Results from numerical and experimental studies show that the proposed algorithm can accurately identify the vehicle’s TP under various conditions and significantly improve the identification accuracy of vehicle weight compared with the traditional Moses’s algorithm.
Secondly, a Bayesian framework for predicting extreme traffic LEs of bridges is presented. The Bayesian method offers a natural framework for uncertainty quantification in parameter estimation and thus can provide more reliable predictions compared with conventional methods. A framework for bridge condition assessment that utilizes the predicted traffic LEs is proposed and a case study on the condition assessment of an instrumented field bridge is presented to demonstrate the proposed methodology. Moreover, the non-stationary Bayesian method is adopted to predict the maximum traffic LEs during the lifetime of bridges subject to different types of traffic growth and the influence of the traffic growth on the bridge safety is investigated.
Yu, Yang, "An Enhanced Bridge Weigh-in-motion Methodology and A Bayesian Framework for Predicting Extreme Traffic Load Effects of Bridges" (2017). LSU Doctoral Dissertations. 4140.