Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

IF 3.4 2区 工程技术 Q2 ENGINEERING, GEOLOGICAL
Yonas Tilahun, Xiao Qinghua, Argaw Asha Ashongo, Xiangyu Han
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引用次数: 0

Abstract

This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (MDD) and optimum moisture content (OMC), which are critical for stabilizing construction foundations. Traditional methods for determining MDD and OMC are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy (E). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for MDD and OMC, outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that MDD and OMC were most influenced by plastic limit (PL), compaction energy (E), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between MDD, OMC, and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.
确定水泥石灰土的压实参数:基于提升的集合模型
本研究调查了人工智能(AI)模型在预测土壤压实特性方面的应用,特别是对稳定建筑地基至关重要的最大干密度(MDD)和最佳含水量(OMC)。确定最大干密度(MDD)和最佳含水量(OMC)的传统方法需要耗费大量人力,而且经常受到土壤类型、塑性和压实能(E)等因素的影响。研究采用了人工智能模型,包括随机森林回归 (RF-R)、梯度提升回归 (GB-R)、XGB 提升回归器 (XGB-R) 和多线性回归 (ML-R),并在全面的土壤特性数据集上进行了训练。压实能首次被用作预测土壤水泥石灰稳定压实参数的输入变量。在这些模型中,GB-R 对 MDD 和 OMC 的预测精度最高,优于 RF-R、XGB-R 和 ML-R。内置模型的性能除了四个常用指标外,还通过三个新的指标性能指标来衡量:a20 指标、分散指标(IS)和一致指标(IA)。泰勒图证实了这些预测在实验室测试中的稳健性。敏感性分析表明,MDD 和 OMC 受塑性极限 (PL)、压实能 (E)、液体极限 (LL) 和塑性指数 (PI) 的影响最大。此外,还采用了曲线拟合技术来模拟 MDD、OMC 与这些关键因素之间的关系。结果表明,与传统回归方法相比,GB-R 模型(尤其是在关注基本特征时)提供了更高的准确性,为建筑工程中的土壤稳定评估提供了可靠的工具。
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来源期刊
CiteScore
6.40
自引率
12.50%
发文量
160
审稿时长
9 months
期刊介绍: The journal welcomes manuscripts that substantially contribute to the understanding of the complex mechanical behaviour of geomaterials (soils, rocks, concrete, ice, snow, and powders), through innovative experimental techniques, and/or through the development of novel numerical or hybrid experimental/numerical modelling concepts in geomechanics. Topics of interest include instabilities and localization, interface and surface phenomena, fracture and failure, multi-physics and other time-dependent phenomena, micromechanics and multi-scale methods, and inverse analysis and stochastic methods. Papers related to energy and environmental issues are particularly welcome. The illustration of the proposed methods and techniques to engineering problems is encouraged. However, manuscripts dealing with applications of existing methods, or proposing incremental improvements to existing methods – in particular marginal extensions of existing analytical solutions or numerical methods – will not be considered for review.
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