Most performance prediction models for asphalt pavements are either based on laboratory data or numerical distress data collected from field surveys. However, these models do not fully reflect the true performance of pavements in different traffic and environmental conditions. In the study reported in this paper, a multi-input unified prediction model based on an artificial neural network was developed by using a mixture of numerical and categorical features for in-service pavement test sections in the USA. Pavement age, cracking length and area, cumulative traffic loading, two functional classes of roads, four climatic zones and maintenance effects were considered as input variables while changes in the pavement condition index (PCI) were determined as the output. The developed model was found to be efficient in terms of processing time and accuracy in dealing with the complexity and non-linearity of multiple input parameters. The results showed that the model provided a high correlation between observed and predicted deterioration at the training stage. The testing and validation results also yielded high accuracy in predicting the PCI and could be combined with a pavement management system to plan timely and accurate maintenance strategies.

中文翻译：

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沥青路面状况预测的统一人工神经网络模型

大多数沥青路面性能预测模型要么基于实验室数据，要么基于现场调查收集的数值损坏数据。然而，这些模型并不能完全反映路面在不同交通和环境条件下的真实性能。在本文报告的研究中，针对美国在役路面试验路段，开发了一种基于人工神经网络的多输入统一预测模型，该模型混合使用了数值特征和分类特征。路面使用年限、裂缝长度和面积、累积交通荷载、道路的两个功能类别、四个气候带和维护效果被视为输入变量，而路面状况指数 (PCI) 的变化被确定为输出。发现所开发的模型在处理多个输入参数的复杂性和非线性方面在处理时间和准确性方面是有效的。结果表明，该模型在训练阶段提供了观察到的和预测的恶化之间的高度相关性。测试和验证结果在预测 PCI 方面也具有很高的准确性，并且可以与路面管理系统相结合，以规划及时准确的维护策略。