An efficient framework of optimized ensemble paradigm for estimating resilient modulus of subgrades

IF 4.9 2区 工程技术 Q1 ENGINEERING, CIVIL
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引用次数: 0

Abstract

This study employs an efficient framework of ensemble and meta-heuristic optimization algorithm for estimating resilient modulus (MR) of subgrades with and without considering the influences of freeze–thaw cycles. Notably, MR is one of the most important stiffness characteristics used in pavement design. The proposed framework combines an ensemble paradigm, random forest regression (RFR), and a widely used meta-heuristic optimization algorithm, grey wolf optimizer (GWO). The outcomes of the established RFR-GWO framework were compared with six regression and neural network-based paradigms namely linear regressor, Gaussian process regression, support vector regressor, artificial neural network, emotional neural network, and multilayer perceptron neural network. For model design and validation, two datasets of A-4, A-6, and A-7–6 (as per AASHTO classification) soils were gathered from the literature. As per experimental results, the developed RFR-GWO achieved the highest degree of accuracy against both datasets with the coefficient of correlation ranging between 0.9970 and 0.9880. To demonstrate the robustness of the established RFR-GWO framework, the impact of the influencing parameters was also investigated via parametric analysis. Overall, the developed RFR-GWO has demonstrated its capability to assist engineers in estimating the subgrade MR during the initial stage of engineering projects.

用于估算子级配弹性模量的优化组合范式的高效框架
本研究采用了一种高效的集合框架和元启发式优化算法,用于估算有无考虑冻融循环影响的基层弹性模量(MR)。值得注意的是,弹性模量是路面设计中最重要的刚度特征之一。所提出的框架结合了一种集合范式--随机森林回归(RFR)和一种广泛使用的元启发式优化算法--灰狼优化器(GWO)。建立的 RFR-GWO 框架的结果与六种基于回归和神经网络的范例进行了比较,即线性回归、高斯过程回归、支持向量回归、人工神经网络、情感神经网络和多层感知器神经网络。为了设计和验证模型,从文献中收集了 A-4、A-6 和 A-7-6(根据 AASHTO 分类)土壤的两个数据集。实验结果表明,所开发的 RFR-GWO 对这两个数据集的准确度最高,相关系数介于 0.9970 和 0.9880 之间。为了证明所建立的 RFR-GWO 框架的稳健性,还通过参数分析研究了影响参数的影响。总体而言,所开发的 RFR-GWO 已证明其有能力帮助工程师在工程项目的初始阶段估算路基 MR。
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来源期刊
Transportation Geotechnics
Transportation Geotechnics Social Sciences-Transportation
CiteScore
8.10
自引率
11.30%
发文量
194
审稿时长
51 days
期刊介绍: Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.
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