{"title":"An efficient framework of optimized ensemble paradigm for estimating resilient modulus of subgrades","authors":"","doi":"10.1016/j.trgeo.2024.101315","DOIUrl":null,"url":null,"abstract":"<div><p>This study employs an efficient framework of ensemble and <em>meta</em>-heuristic optimization algorithm for estimating resilient modulus (M<sub>R</sub>) of subgrades with and without considering the influences of freeze–thaw cycles. Notably, M<sub>R</sub> 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 <em>meta</em>-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 M<sub>R</sub> during the initial stage of engineering projects.</p></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214391224001363","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
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.
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.