Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils
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 engineer...
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Elsevier
2024-12-01
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| Series: | Scientific African |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227624003351 |
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| author | Lateef Bankole Adamolekun Muyideen Alade Saliu Abiodun Ismail Lawal Ismail Adeniyi Okewale |
| author_facet | Lateef Bankole Adamolekun Muyideen Alade Saliu Abiodun Ismail Lawal Ismail Adeniyi Okewale |
| author_sort | Lateef Bankole Adamolekun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-78126a0b678a496db4d52b72135cf23f |
| institution | Kabale University |
| issn | 2468-2276 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj-art-78126a0b678a496db4d52b72135cf23f2024-12-21T04:29:03ZengElsevierScientific African2468-22762024-12-0126e02393Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soilsLateef Bankole Adamolekun0Muyideen Alade Saliu1Abiodun Ismail Lawal2Ismail Adeniyi Okewale3Corresponding author.; Department of Mining Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, NigeriaDepartment of Mining Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, NigeriaDepartment of Mining Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, NigeriaDepartment of Mining Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, NigeriaHydraulic 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.http://www.sciencedirect.com/science/article/pii/S2468227624003351Lateritic soilHydraulic conductivityCompaction parametersArtificial neural networkGraphical user interfaceStandalone application |
| spellingShingle | Lateef Bankole Adamolekun Muyideen Alade Saliu Abiodun Ismail Lawal Ismail Adeniyi Okewale Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils Scientific African Lateritic soil Hydraulic conductivity Compaction parameters Artificial neural network Graphical user interface Standalone application |
| title | Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils |
| title_full | Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils |
| title_fullStr | Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils |
| title_full_unstemmed | Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils |
| title_short | Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils |
| title_sort | development of machine learning based standalone gui application for predicting hydraulic conductivity and compaction parameters of lateritic soils |
| topic | Lateritic soil Hydraulic conductivity Compaction parameters Artificial neural network Graphical user interface Standalone application |
| url | http://www.sciencedirect.com/science/article/pii/S2468227624003351 |
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