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.

这项研究检查了人工神经网络（ANN）的适用性，以进行精确的额定载荷估算和桥梁管理。大梁桥的桥梁管理通常依赖于AASHTO线大梁（1D）分析。AASHTO允许使用不太保守，更严格的方法，但会带来不确定的投资回报。这项研究表明，少量的完善分析以及人工神经网络可以用作预测人群中类似方法和桥梁的可能结果的预测工具。考虑了两个基于ANN的负荷预测模型：（1）最佳网络和（2）委员会网络（CN）。在混合子集上检查了额定载荷预测的准确性，该子集由代表钢梁桥梁库存的假设桥梁和真实桥梁组成。训练了基于ANN的预测模型，以将控制输入（结构和几何桥梁特性）映射到从3D FE分析获得的额定载荷。对训练子集之外的桥梁的预测准确性表明，使用经过适当训练的具有优化模型复杂性的网络模型，可以可靠地估算出精确的额定载荷（平均绝对误差约为5％）。与单一最佳网络方法相比，CN模型提供了更高的预测准确性和更高的置信度，并且随着训练样本量的减少，对人口规模的敏感性降低。对训练子集之外的桥梁的预测准确性表明，使用经过适当训练的具有优化模型复杂性的网络模型，可以可靠地估算出精确的额定载荷（平均绝对误差约为5％）。与单一最佳网络方法相比，CN模型提供了更高的预测准确性和更高的置信度，并且随着训练样本量的减少，对人口规模的敏感性降低。对训练子集之外的桥梁的预测准确性表明，使用经过适当训练的具有优化模型复杂性的网络模型，可以可靠地估算出精确的额定载荷（平均绝对误差约为5％）。与单一最佳网络方法相比，CN模型提供了更高的预测准确性和更高的置信度，并且随着训练样本量的减少，对人口规模的敏感性降低。