Purpose

Unbonded concrete overlays (UBOLs) are commonly used in pavement rehabilitation. The current models included in the Mechanistic-Empirical Pavement Design Guide cannot properly predict the structural response of UBOLs. In this paper, a multigene genetic programming (MGGP) approach is proposed to derive new prediction models for the UBOLs response to temperature loading.

Design/methodology/approach

MGGP is a promising variant of evolutionary computation capable of developing highly nonlinear explicit models for characterizing complex engineering problems. The proposed UBOL response models are formulated in terms of several influencing parameters including joint spacing, radius of relative stiffness, temperature gradient and adjusted load/pavement weight ratio. Furthermore, linear regression models are developed to benchmark the MGGP models.

Findings

The derived design equations accurately characterize the UBOLs response under temperature loading and remarkably outperform the regression models. The conducted parametric analysis implies the efficiency of the MGGP-based model in capturing the underlying physical behavior of the UBOLs response to temperature loading. Based on the results, the proposed models can be reliably deployed for design purposes.

Originality/value

A challenge in the design of UBOLs is that their interlayer effects have not been directly considered in previous design procedures. To achieve better performance predictions, it is necessary to capture the effect of the interlayer in the design process. This study addresses this important issue via developing new models that can efficiently account for the effects of interlayer on the stress and deflections. In addition, it provides an insight into the effect of several parameters influencing the deflections of the UBOLs. From a computing perspective, a powerful evolutionary computation technique is introduced that overcomes the shortcomings of existing machine learning methods.

中文翻译：

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一种演化计算方法来制定未粘合混凝土覆盖层对温度载荷的响应

目的

未粘合混凝土覆盖层 (UBOL) 通常用于路面修复。机械经验路面设计指南中包含的当前模型无法正确预测 UBOL 的结构响应。在本文中，提出了一种多基因遗传编程 (MGGP) 方法来推导 UBOL 对温度负载响应的新预测模型。

设计/方法/方法

MGGP 是一种很有前途的进化计算变体，能够开发高度非线性的显式模型来表征复杂的工程问题。所提出的 UBOL 响应模型是根据几个影响参数制定的，包括接缝间距、相对刚度半径、温度梯度和调整后的荷载/路面重量比。此外，还开发了线性回归模型来对 MGGP 模型进行基准测试。

发现

导出的设计方程准确地描述了温度负载下的 UBOL 响应，并显着优于回归模型。进行的参数分析意味着基于 MGGP 的模型在捕获 UBOL 对温度负载响应的潜在物理行为方面的效率。根据结果​​，所提出的模型可以可靠地部署用于设计目的。

原创性/价值

UBOL 设计中的一个挑战是在以前的设计程序中没有直接考虑它们的层间效应。为了实现更好的性能预测，有必要在设计过程中捕捉到中间层的效果。本研究通过开发新模型来解决这一重要问题，该模型可以有效地解释夹层对应力和变形的影响。此外，它还提供了对影响 UBOL 偏转的几个参数的影响的洞察。从计算的角度来看，引入了一种强大的进化计算技术，克服了现有机器学习方法的缺点。