Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils

IF 2.7 Q2 MULTIDISCIPLINARY SCIENCES Scientific African Pub Date : 2024-09-17 DOI:10.1016/j.sciaf.2024.e02393
Lateef Bankole Adamolekun, Muyideen Alade Saliu, Abiodun Ismail Lawal, Ismail Adeniyi Okewale
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Abstract

Hydraulic conductivity and compaction parameters are the key considerations in selecting lateritic soils for engineering construction. Nevertheless, the complexity and high cost of the required tests have driven many contractors and field engineers to skip them, resulting in a succession of engineering structure failures. To overcome this limitation, this study developed machine learning-based standalone GUI application to predict lateritic soils’ hydraulic conductivity (K), maximum dry density (MDD) and optimum moisture content (OMC) from indices including specific gravity, liquid limit, plasticity index, linear shrinkage and fine content. To achieve this goal, the geotechnical properties of three hundred samples, collected using grid sampling method from thirty different lateritic deposits in southwestern Nigeria, were evaluated through laboratory tests. The test results were used to train predictive models using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and Gaussian process regression (GPR). The models’ performance was compared using coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). Based on these performance metrics, ANN demonstrated the best performance (R2 = 0.9835, 0.9797, 0.9999; RMSE = 7.938, 0.252, 2.09E-08; MAPE = 0.288, 1.114, 1.587; MAE = 5.432, 0.169, 1.1E-08) for MDD, OMC and K, respectively, followed by GPR and then ANFIS. Thus, the ANN models were selected and embedded in a standalone GUI application to enhance easy and quick prediction of lateritic soils’ MDD, OMC and K. The validity of the ANN-based standalone GUI application was demonstrated by comparing it favorably to notable regression-based models in the literature.
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开发基于机器学习的独立 GUI 应用程序,用于预测红土的导水性和压实参数
导水性和压实参数是选择红土用于工程建设的关键因素。然而,所需测试的复杂性和高成本促使许多承包商和现场工程师跳过这些测试,导致工程结构接连失败。为克服这一局限性,本研究开发了基于机器学习的独立 GUI 应用程序,可根据比重、液限、塑性指数、线性收缩和细粒含量等指标预测红土的导水性(K)、最大干密度(MDD)和最佳含水量(OMC)。为实现这一目标,通过实验室测试评估了从尼日利亚西南部 30 个不同红土矿床中采用网格取样法收集的 300 个样本的岩土特性。测试结果用于使用人工神经网络 (ANN)、自适应神经模糊推理系统 (ANFIS) 和高斯过程回归 (GPR) 训练预测模型。使用判定系数 (R2)、均方根误差 (RMSE)、平均绝对百分比误差 (MAPE) 和平均绝对误差 (MAE) 对模型的性能进行了比较。根据这些性能指标,ANN 对 MDD、OMC 和 K 的性能最好(R2 = 0.9835、0.9797、0.9999;RMSE = 7.938、0.252、2.09E-08;MAPE = 0.288、1.114、1.587;MAE = 5.432、0.169、1.1E-08),其次是 GPR,然后是 ANFIS。因此,选择了 ANN 模型并将其嵌入到独立的 GUI 应用程序中,以提高红土土壤 MDD、OMC 和 K 预测的简便性和快速性。
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来源期刊
Scientific African
Scientific African Multidisciplinary-Multidisciplinary
CiteScore
5.60
自引率
3.40%
发文量
332
审稿时长
10 weeks
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