Investigating the competency of some selected soft computing techniques for modeling of lateritic soil strength based on index properties

Lateef Bankole Adamolekun, M. Saliu, A. Lawal, I. A. Okewale
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Abstract

This study aims to assess the capability of some soft computing techniques including ANN, M5P and RF to accurately predict the strength of selected lateritic soils in southwestern Nigeria from index properties including specific gravity, linear shrinkage, liquid limit, plasticity index, fine sand content, and fines content. To achieve this goal, the experimental dataset obtained from the laboratory analysis of three hundred soil samples taken from thirty different lateritic deposits within southwestern Nigeria was divided into model and gaging dataset. The model dataset contains two hundred and forty data points, which were divided into 70% for training and 15% each for testing and validation of the proposed models. The gaging dataset contains sixty data points, which were used to validate the proposed models against prominent existing models in the literature. The models performances were evaluated using various statistical estimators. Based on the statistical estimators, the proposed models outperformed the existing models in the literature and provided satisfactory performances, thus, they are validated. The obtained R2 values using the ANN model are 0.9967, 0.9963, 0.9989, and 0.9852 for training, testing, validation, and gaging dataset, respectively; the R2 values obtained for M5P model are 0.6676, 0.5501, 0.636 and 0.6727; and the R2 values for RF model are 0.8346, 0.6380, 0.7564, and 0.7901. This implies that ANN provided the most reliable model for the prediction of the soil strength. Thus, ANN is strongly recommended for prediction of lateritic soil strength.
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基于指数特性的红土强度建模软计算技术能力研究
本研究旨在评估一些软计算技术(包括 ANN、M5P 和 RF)的能力,以根据指标属性(包括比重、线性收缩率、液限、塑性指数、细砂含量和细粒含量)准确预测尼日利亚西南部选定红土的强度。为实现这一目标,对尼日利亚西南部三十个不同红土矿床的三百个土壤样本进行实验室分析后获得的实验数据集被分为模型数据集和测量数据集。模型数据集包含 240 个数据点,其中 70% 用于训练,15% 用于测试和验证模型。测量数据集包含六十个数据点,用于对照文献中现有的著名模型验证所提出的模型。使用各种统计估算器对模型的性能进行了评估。根据统计估算器,所提出的模型优于文献中的现有模型,并提供了令人满意的性能,因此这些模型得到了验证。对于训练、测试、验证和测量数据集,使用 ANN 模型获得的 R2 值分别为 0.9967、0.9963、0.9989 和 0.9852;M5P 模型获得的 R2 值分别为 0.6676、0.5501、0.636 和 0.6727;RF 模型获得的 R2 值分别为 0.8346、0.6380、0.7564 和 0.7901。这意味着,方差网络为土壤强度预测提供了最可靠的模型。因此,强烈建议将 ANN 用于红土强度的预测。
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