Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea)
The compression index (C<sub>c</sub>) is a critical soil parameter that is used to estimate the consolidation settlement of ground. In this study, the compression index, typically obtained through consolidation tests, was predicted using machine learning techniques after preprocessing da...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2757 |
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| author | Seungyeon Ryu Jin Kim Hyoyeop Choi Jongyoung Lee Junggeun Han |
| author_facet | Seungyeon Ryu Jin Kim Hyoyeop Choi Jongyoung Lee Junggeun Han |
| author_sort | Seungyeon Ryu |
| collection | DOAJ |
| description | The compression index (C<sub>c</sub>) is a critical soil parameter that is used to estimate the consolidation settlement of ground. In this study, the compression index, typically obtained through consolidation tests, was predicted using machine learning techniques after preprocessing data that considered the geotechnical and hydrogeological characteristics of the study area. This approach enabled an analysis of how geotechnical and hydrogeological characteristics affect the performance of machine learning models. Data obtained from geotechnical investigations were used to train models for each classified zone. Suitable models were then selected to predict the compression index, and their performance was evaluated. Predictions that considered the geotechnical and hydrogeological characteristics showed improved accuracy in zones influenced by a single water system or zones near the coast. However, in offshore areas with complex water systems, using the entire dataset proved to be more effective. Differences in the clay mineral of the soil also affected the prediction accuracy, indicating a correlation between clay mineral properties and model performance. These findings suggest that classifying data based on geotechnical and hydrogeological characteristics is necessary when developing compression index prediction models to achieve relatively stable results. |
| format | Article |
| id | doaj-art-99c2914d354646689d9f4adbc0be02bb |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-99c2914d354646689d9f4adbc0be02bb2025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-03-01155275710.3390/app15052757Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea)Seungyeon Ryu0Jin Kim1Hyoyeop Choi2Jongyoung Lee3Junggeun Han4Department of Intelligent Energy and Industry, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Intelligent Energy and Industry, Chung-Ang University, Seoul 06974, Republic of KoreaInfrastructure Division, Saemangeum Development and Investment Agency, Gunsan 54004, Republic of KoreaSchool of Civil and Environmental Engineering, Urban Design and Study, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Intelligent Energy and Industry, Chung-Ang University, Seoul 06974, Republic of KoreaThe compression index (C<sub>c</sub>) is a critical soil parameter that is used to estimate the consolidation settlement of ground. In this study, the compression index, typically obtained through consolidation tests, was predicted using machine learning techniques after preprocessing data that considered the geotechnical and hydrogeological characteristics of the study area. This approach enabled an analysis of how geotechnical and hydrogeological characteristics affect the performance of machine learning models. Data obtained from geotechnical investigations were used to train models for each classified zone. Suitable models were then selected to predict the compression index, and their performance was evaluated. Predictions that considered the geotechnical and hydrogeological characteristics showed improved accuracy in zones influenced by a single water system or zones near the coast. However, in offshore areas with complex water systems, using the entire dataset proved to be more effective. Differences in the clay mineral of the soil also affected the prediction accuracy, indicating a correlation between clay mineral properties and model performance. These findings suggest that classifying data based on geotechnical and hydrogeological characteristics is necessary when developing compression index prediction models to achieve relatively stable results.https://www.mdpi.com/2076-3417/15/5/2757machine learningsoft groundcompression indexSaemangeum reclaimed tidal landbedrock |
| spellingShingle | Seungyeon Ryu Jin Kim Hyoyeop Choi Jongyoung Lee Junggeun Han Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea) Applied Sciences machine learning soft ground compression index Saemangeum reclaimed tidal land bedrock |
| title | Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea) |
| title_full | Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea) |
| title_fullStr | Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea) |
| title_full_unstemmed | Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea) |
| title_short | Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea) |
| title_sort | case study on analysis of soil compression index prediction performance using linear and regularized linear machine learning models in korea |
| topic | machine learning soft ground compression index Saemangeum reclaimed tidal land bedrock |
| url | https://www.mdpi.com/2076-3417/15/5/2757 |
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