Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing
Marine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse...
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MDPI AG
2025-07-01
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| author | Bowen Yang Kaiwei Xu Zejin Wang Haodong Sun Peng Cui Zhiming Chao |
| author_facet | Bowen Yang Kaiwei Xu Zejin Wang Haodong Sun Peng Cui Zhiming Chao |
| author_sort | Bowen Yang |
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| description | Marine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse conditions, including variations in asperity spacing, asperity height, the number of reinforcement layers, confining pressure, and axial strain. This experimental campaign yielded a robust strength dataset for MCCM. Utilizing this dataset, several predictive models were developed, including a standard Support Vector Machine (SVM), an SVM optimized via Genetic Algorithm (GA-SVM), an SVM enhanced by Particle Swarm Optimization (PSO-SVM), and a hybrid model incorporating Logical Development Algorithm preprocessing a SVM model (LDA-SVM). Among these models, the LDA-SVM model exhibited the best performance, achieving a test RMSE of 1.67245 and a correlation coefficient (R) of 0.996, demonstrating superior prediction accuracy and strong generalization ability. Sensitivity analyses revealed that asperity spacing, asperity height, and confining pressure are the most influential factors affecting MCCM strength. Moreover, an explicit empirical equation was derived from the LDA-SVM model, allowing practitioners to estimate strength without relying on complex machine learning tools. The results of this study offer practical guidance for the optimized design and safety evaluation of MCCM in civil engineering applications. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-b5cfcfdfd5ea44b4b8c4c930b772fa1b2025-08-20T03:35:27ZengMDPI AGBuildings2075-53092025-07-011514248110.3390/buildings15142481Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear TestingBowen Yang0Kaiwei Xu1Zejin Wang2Haodong Sun3Peng Cui4Zhiming Chao5Shanxi Ning Guli New Materials Joint Stock Company Limited, Jinzhong 030800, ChinaCollege of Marine Science and Engineering, Shanghai Maritime University, Shanghai 200135, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing 211816, ChinaWeifang Hydraulic Architectural Design and Research Institute Co., Ltd., Weifang 261000, ChinaSchool of Civil Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Marine Science and Engineering, Shanghai Maritime University, Shanghai 200135, ChinaMarine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse conditions, including variations in asperity spacing, asperity height, the number of reinforcement layers, confining pressure, and axial strain. This experimental campaign yielded a robust strength dataset for MCCM. Utilizing this dataset, several predictive models were developed, including a standard Support Vector Machine (SVM), an SVM optimized via Genetic Algorithm (GA-SVM), an SVM enhanced by Particle Swarm Optimization (PSO-SVM), and a hybrid model incorporating Logical Development Algorithm preprocessing a SVM model (LDA-SVM). Among these models, the LDA-SVM model exhibited the best performance, achieving a test RMSE of 1.67245 and a correlation coefficient (R) of 0.996, demonstrating superior prediction accuracy and strong generalization ability. Sensitivity analyses revealed that asperity spacing, asperity height, and confining pressure are the most influential factors affecting MCCM strength. Moreover, an explicit empirical equation was derived from the LDA-SVM model, allowing practitioners to estimate strength without relying on complex machine learning tools. The results of this study offer practical guidance for the optimized design and safety evaluation of MCCM in civil engineering applications.https://www.mdpi.com/2075-5309/15/14/2481marine coral sand–clay mixturestrength predictionLDA-SVM modelmachine learningtriaxial shear test |
| spellingShingle | Bowen Yang Kaiwei Xu Zejin Wang Haodong Sun Peng Cui Zhiming Chao Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing Buildings marine coral sand–clay mixture strength prediction LDA-SVM model machine learning triaxial shear test |
| title | Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing |
| title_full | Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing |
| title_fullStr | Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing |
| title_full_unstemmed | Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing |
| title_short | Machine Learning Prediction of Mechanical Properties for Marine Coral Sand–Clay Mixtures Based on Triaxial Shear Testing |
| title_sort | machine learning prediction of mechanical properties for marine coral sand clay mixtures based on triaxial shear testing |
| topic | marine coral sand–clay mixture strength prediction LDA-SVM model machine learning triaxial shear test |
| url | https://www.mdpi.com/2075-5309/15/14/2481 |
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