Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine
Abstract Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84632-5 |
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| _version_ | 1841559750063423488 |
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| author | Hanliang Bian Zhongxun Sun Jiahan Bian Zhaowei Qu Jianwei Zhang Xiangchun Xu |
| author_facet | Hanliang Bian Zhongxun Sun Jiahan Bian Zhaowei Qu Jianwei Zhang Xiangchun Xu |
| author_sort | Hanliang Bian |
| collection | DOAJ |
| description | Abstract Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ( $$q_c$$ ) and sleeve friction ( $$f_s$$ ) as input variables. Pearson correlation and sensitivity analysis confirmed that these variables are highly correlated with the classification results. To enhance classification performance, 25 optimization algorithms were applied, and the models were validated against an independent dataset of 208 CPT records. The results revealed that 23 of the algorithms successfully improved the SVM classification accuracy. Among these, 18 algorithms achieved higher accuracy than the current engineering standard, the “Code for in-situ Measurement of Railway Engineering Geology.” Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. Moreover, the models were thoroughly evaluated using Monte Carlo simulations, confusion matrices, ROC curves, and 10 key performance metrics. In conclusion, integrating evolutionary algorithms with SVM for soil classification offers a promising approach to enhancing the efficiency and accuracy of soil analysis in engineering applications. |
| format | Article |
| id | doaj-art-8547ec0d6c744e75acbe93e28e8b87de |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8547ec0d6c744e75acbe93e28e8b87de2025-01-05T12:14:18ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84632-5Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machineHanliang Bian0Zhongxun Sun1Jiahan Bian2Zhaowei Qu3Jianwei Zhang4Xiangchun Xu5School of Civil Engineering and Architecture, Henan UniversitySchool of Civil Engineering and Architecture, Henan UniversityXiang Yang HangTai Power Machinery PlantJiuzhou Engineering Design Co., LtdSchool of Civil Engineering and Architecture, Henan UniversitySchool of Civil Engineering and Architecture, Henan UniversityAbstract Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ( $$q_c$$ ) and sleeve friction ( $$f_s$$ ) as input variables. Pearson correlation and sensitivity analysis confirmed that these variables are highly correlated with the classification results. To enhance classification performance, 25 optimization algorithms were applied, and the models were validated against an independent dataset of 208 CPT records. The results revealed that 23 of the algorithms successfully improved the SVM classification accuracy. Among these, 18 algorithms achieved higher accuracy than the current engineering standard, the “Code for in-situ Measurement of Railway Engineering Geology.” Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. Moreover, the models were thoroughly evaluated using Monte Carlo simulations, confusion matrices, ROC curves, and 10 key performance metrics. In conclusion, integrating evolutionary algorithms with SVM for soil classification offers a promising approach to enhancing the efficiency and accuracy of soil analysis in engineering applications.https://doi.org/10.1038/s41598-024-84632-5 |
| spellingShingle | Hanliang Bian Zhongxun Sun Jiahan Bian Zhaowei Qu Jianwei Zhang Xiangchun Xu Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine Scientific Reports |
| title | Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine |
| title_full | Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine |
| title_fullStr | Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine |
| title_full_unstemmed | Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine |
| title_short | Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine |
| title_sort | assessment of soil classification based on cone penetration test data for kaifeng area using optimized support vector machine |
| url | https://doi.org/10.1038/s41598-024-84632-5 |
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