ABSTRACT

A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind model simultaneously predicts four pavement condition metrics concerning rigid pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate pavement conditions and to make treatment decisions based on multi-condition metrics, such as the pavement condition index (PCI) for budget allocation models. Compared to traditional single-output NN models, this multi-output model is capable of incorporating correlations among different condition metrics. During model training, each condition metric is assigned a weight to reflect its relative importance. When the weights equal to those in the formula for the multi-condition metric, the prediction performance for PCI is optimal (13% lower MSE than optimal, single-output models). The multi-output model improves on the prediction performance for three of the four individual condition metrics compared to optimal single-output models. Results show that the consideration of correlations could improve the prediction performance for single and multi-condition metrics. Finally, variable weighting is critical for achieving the optimal balance of prediction performance among the various metrics as dictated by the needs of the decisionmaker.

摘要

提出了一种基于爱荷华州路面管理系统数据的新型加权多输出神经网络模型，用于预测刚性路面的劣化。该首创模型同时预测了与刚性路面有关的四个路面状况指标，包括IRI，断层，纵向裂缝和横向裂缝。它提供了一个机会，可以有效地评估路面状况并基于多条件指标（例如预算分配模型的路面状况指数（PCI））做出处理决策。与传统的单输出NN模型相比，该多输出模型能够合并不同条件度量之间的相关性。在模型训练期间，为每个条件量度分配一个权重以反映其相对重要性。当权重等于多条件度量公式中的权重时，PCI的预测性能最佳（MSE比最佳单输出模型低13％）。与最佳单输出模型相比，多输出模型改善了四个单独条件量度中的三个的预测性能。结果表明，相关性的考虑可以提高单条件和多条件指标的预测性能。最后，变量权重对于在决策者的需求所决定的各种指标之间实现最佳的预测性能平衡至关重要。与最佳单输出模型相比，多输出模型改善了四个单独条件量度中的三个的预测性能。结果表明，相关性的考虑可以提高单条件和多条件指标的预测性能。最后，变量权重对于在决策者的需求所决定的各种指标之间实现最佳的预测性能平衡至关重要。与最佳单输出模型相比，多输出模型改善了四个单独条件量度中的三个的预测性能。结果表明，相关性的考虑可以提高单条件和多条件指标的预测性能。最后，变量权重对于在决策者的需求所决定的各种指标之间实现最佳的预测性能平衡至关重要。