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Models Relating Pavement Quality Measures

机译：与路面质量度量有关的模型

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A large field-measured international roughness index (IRI) data set was collected in the summer and fall of 2001 on 650 highway kilometers with sections containing full-depth hot-mixed asphalt (HMA) and HMA over Portland cement; the set was used to investigate the relationships between IRI, rutting, and pavement cracking. Parametric statistical analysis, linear regression, and neural network models were used with a large database of 65,530 observations (10-m intervals). The goal was to investigate the relationship between both rutting and cracking (estimated from photolog images obtained at the same time field data were collected) and IRI. Specifically, the objective was to determine if these relationships were consistent enough to allow IRI, the more easily measured quantity, to be used as a surrogate for the others or if the effort and resources to collect all three measures were necessary in pavement management programs. The results indicate that while statistically significant relationships exist between IRI and both cracking and rutting, these relationships are not strong enough for IRI to be used as a surrogate measure for pavement condition. The weak predictive relationships are further evidenced by the lack of success in training a neural network that could significantly outperform linear regression models. It was concluded that IRI, while appropriate for measuring rideability, was not appropriate for measuring cracking or rutting.
机译：2001年夏季和秋季，在650高速公路公里上收集了一个大型现场测量的国际粗糙度指数（IRI）数据集，这些剖面包含全深度热混合沥青（HMA）和波特兰水泥上的HMA。该集合用于研究IRI，车辙和路面开裂之间的关系。参数统计分析，线性回归和神经网络模型与65,530个观测值（间隔为10米）的大型数据库一起使用。目的是研究车辙和裂纹之间的关系（从收集现场数据的同时获得的光日志图像估计）和IRI。具体而言，目标是确定这些关系是否足够一致，以允许将更容易测量的IRI用作其他指标的替代，或者在路面管理计划中是否需要收集所有这三种量度的精力和资源。结果表明，尽管IRI与开裂和车辙之间存在统计上显着的关系，但这些关系不足以使IRI用作路面状况的替代度量。在训练神经网络方面缺乏成功，这进一步证明了较弱的预测关系，该神经网络的性能可能大大优于线性回归模型。得出的结论是，IRI虽然适合测量骑乘性，但不适合测量裂纹或车辙。

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