Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study
Abstract Expansive soils present substantial challenges for construction projects, particularly in arid regions where their swelling and shrinking behavior can result in structural damage. This research conducted an in-depth analysis of 195 soil samples, investigating six crucial factors, including...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09279-2 |
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| author | Sultan Almuaythir Muhammad Syamsul Imran Zaini Rida Hameed Lodhi |
| author_facet | Sultan Almuaythir Muhammad Syamsul Imran Zaini Rida Hameed Lodhi |
| author_sort | Sultan Almuaythir |
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| description | Abstract Expansive soils present substantial challenges for construction projects, particularly in arid regions where their swelling and shrinking behavior can result in structural damage. This research conducted an in-depth analysis of 195 soil samples, investigating six crucial factors, including clay content, liquid and plastic limit, specific gravity, plasticity index, and swell percentage, influencing soil compaction. The study used five computational models to predict optimal water content and maximum dry density. Among the models, XG-Boost exhibited exceptional performance with high predictive accuracy, scoring 0.941 and 0.912 for water content and density, respectively. Random Forest method also performed well, whereas Long Short-Term Memory Network (LSTMN) and k-Nearest Neighbors methods achieved moderate success. Detailed performance metrics, including correlation coefficients (r), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), and performance index (PI), were analyzed for each model across the training, testing, and validation datasets. XG-Boost consistently outperformed other models, demonstrating its robust and reliable predictive power. The analysis of absolute error highlighted the accuracy and consistency of XG-Boost and Random Forest in predicting the maximum dry density. |
| format | Article |
| id | doaj-art-4e0f02a2f9474afaa77d078988f05f8b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4e0f02a2f9474afaa77d078988f05f8b2025-08-20T03:38:12ZengNature PortfolioScientific Reports2045-23222025-07-0115112110.1038/s41598-025-09279-2Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative studySultan Almuaythir0Muhammad Syamsul Imran Zaini1Rida Hameed Lodhi2Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz UniversityFaculty of Civil Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil YaakobDepartment of Urban and Regional Planning, National University of Sciences and Technology (NUST)Abstract Expansive soils present substantial challenges for construction projects, particularly in arid regions where their swelling and shrinking behavior can result in structural damage. This research conducted an in-depth analysis of 195 soil samples, investigating six crucial factors, including clay content, liquid and plastic limit, specific gravity, plasticity index, and swell percentage, influencing soil compaction. The study used five computational models to predict optimal water content and maximum dry density. Among the models, XG-Boost exhibited exceptional performance with high predictive accuracy, scoring 0.941 and 0.912 for water content and density, respectively. Random Forest method also performed well, whereas Long Short-Term Memory Network (LSTMN) and k-Nearest Neighbors methods achieved moderate success. Detailed performance metrics, including correlation coefficients (r), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), and performance index (PI), were analyzed for each model across the training, testing, and validation datasets. XG-Boost consistently outperformed other models, demonstrating its robust and reliable predictive power. The analysis of absolute error highlighted the accuracy and consistency of XG-Boost and Random Forest in predicting the maximum dry density.https://doi.org/10.1038/s41598-025-09279-2Compaction characteristicsExpansive clay soilMachine learing approachesPrediction model |
| spellingShingle | Sultan Almuaythir Muhammad Syamsul Imran Zaini Rida Hameed Lodhi Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study Scientific Reports Compaction characteristics Expansive clay soil Machine learing approaches Prediction model |
| title | Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study |
| title_full | Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study |
| title_fullStr | Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study |
| title_full_unstemmed | Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study |
| title_short | Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study |
| title_sort | predicting soil compaction parameters in expansive soils using advanced machine learning models a comparative study |
| topic | Compaction characteristics Expansive clay soil Machine learing approaches Prediction model |
| url | https://doi.org/10.1038/s41598-025-09279-2 |
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