Using committees of artificial neural networks with finite element modeling for steel girder bridge load rating estimation

Abstract

This study examines the suitability of artificial neural networks (ANNs) for refined load rating estimation and bridge management. Bridge management for girder bridges commonly relies on AASHTO line girder (1D) analyses. Less conservative, more rigorous methods are permitted by AASHTO, but provide uncertain return on investment. This study demonstrates that a small set of refined analyses, coupled with ANNs, can be used as a predictive tool to anticipate the likely outcome for similar methods and bridges in a population. Two ANN-based load-rating prediction models were considered: (1) single-best-network, and (2) committee networks (CN). Load rating prediction accuracy was examined on a hybrid subset, consisting of hypothetical and real bridges representative of a steel girder bridge inventory. ANN-based prediction models were trained to map governing inputs (structural and geometric bridge characteristics) to load ratings obtained from 3D FE analyses. Prediction accuracy for bridges outside the training subset demonstrated that refined load ratings can be reliably estimated (about 5% mean absolute error) using a properly trained network model with optimized model complexity. The CN model provided improved prediction accuracy with higher confidence levels than the single-best-network approach, and exhibits less sensitivity to population size as the training sample size was reduced.