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首页> 外文期刊>Journal of materials in civil engineering >Estimating Asphalt Concrete Modulus of Existing Flexible Pavements for Mechanistic-Empirical Rehabilitation Analyses
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Estimating Asphalt Concrete Modulus of Existing Flexible Pavements for Mechanistic-Empirical Rehabilitation Analyses

机译：估算现有柔性路面的沥青混凝土模量，以进行机械-经验修复分析

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摘要

The modulus of the existing asphalt concrete (AC) layer, back-calculated from nondestructive pavement tests, is a crucial input for an accurate overlay design in the pavement mechanistic-empirical (ME) design system. However, nondestructive testing (NDT) data for this purpose are not always available for network-level rehabilitation analyses. To address this issue, this paper proposes a regularized regression method to accurately estimate the moduli with data readily available from pavement management systems, including distress, structural information, and climatic conditions. The data from the Long-Term Pavement Performance (LTPP) database were used for model training. Prediction performance comparisons among three regularization regression methods (ridge, elastic net, and lasso) and the ordinary least-squares regression were conducted. The results showed that the elastic net regression outperformed the other three methods in terms of predictability and interpretability. The mean squared errors of the regularization regression methods were found to be considerably lower than that of the ordinary least-squares regression. The moduli estimated by the regularization methods were very close to the back-calculated ones from the LTPP database, which demonstrated the feasibility of estimating the moduli of existing pavement when in paucity of NDT data. After applying the estimated moduli in the pavement ME design system, the predicted alligator cracking was closer to the measured data than those without these data.
机译：从无损路面测试中反算得出的现有沥青混凝土（AC）层的模量，是路面机械经验（ME）设计系统中进行精确铺面设计的关键输入。但是，用于此目的的非破坏性测试（NDT）数据并不总是可用于网络级的恢复分析。为了解决这个问题，本文提出了一种正规化的回归方法，可以利用路面管理系统提供的数据（包括遇险，结构信息和气候条件）准确估算模量。来自长期路面性能（LTPP）数据库的数据用于模型训练。进行了三种正则化回归方法（岭，弹性网和套索）和普通最小二乘回归之间的预测性能比较。结果表明，弹性净回归在可预测性和可解释性方面优于其他三种方法。发现正则化回归方法的均方误差显着低于普通最小二乘回归的均方误差。通过正则化方法估算的模量与LTPP数据库中的反算模量非常接近，这证明了在缺乏NDT数据时估算现有路面模量的可行性。在路面ME设计系统中应用估计的模量后，与没有这些数据的鳄鱼相比，预测的鳄鱼开裂更接近实测数据。

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