Multi-Modal NDE Data Analysis for Bridge Assessment Using the BEAST Dataset and Temporal Graph Convolution Networks

Abstract

Preserving aging bridges, which are vital to transportation networks, presents notable difficulties due to factors like intense usage, structural wear, and restricted maintenance resources. This research examines the deployment of Nondestructive Evaluation (NDE) techniques to optimize bridge maintenance strategies and maintain structural soundness. Over the course of infrastructure lifespans, vast amounts of NDE data are accumulated, yet processing and interpreting this information proves challenging due to intricate spatial and temporal interdependencies. In this study, we approach the problem as one of graph-based prediction, introducing two advanced methodologies to address it. The primary approach utilizes a Temporal Graph Convolution Network (TGCN), harnessing spatio-temporal patterns for predictive modeling. The secondary approach, a multi-modal TGCN, integrates data fusion techniques to combine diverse data sources for improved predictive accuracy. We evaluate the performance of these approaches using NDE data collected at Rutgers’ BEAST^® facility that includes five NDE modalities and 14 consecutive time intervals for assessing bridge deck conditions, comparing the results against a baseline Spatio-Temporal Autoregressive (STAR) model. While the STAR model established foundational forecasts, the TGCN method achieved superior results by managing nonlinearities. The multi-modal TGCN further enhanced performance, demonstrating the advantages of leveraging data fusion to incorporate multiple data types within TGCN frameworks.