Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling
Abstract In geotechnical engineering, it is crucial to make sure that the undrained shear strength (USS) of soft, sensitive clays is accurately assessed. The accuracy in forecasting USS is pivotal for ensuring the structural integrity and stability of foundations and earthworks. Addressing this conc...
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| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-025-00586-z |
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| author | Chen Fang Ying Li Yang Shi |
| author_facet | Chen Fang Ying Li Yang Shi |
| author_sort | Chen Fang |
| collection | DOAJ |
| description | Abstract In geotechnical engineering, it is crucial to make sure that the undrained shear strength (USS) of soft, sensitive clays is accurately assessed. The accuracy in forecasting USS is pivotal for ensuring the structural integrity and stability of foundations and earthworks. Addressing this concern, advanced data-driven NB techniques are utilized to disclose the complex interactions of USS with basic soil parameters. This paper presents a novel methodology for the USS prediction in soft clays using machine learning techniques, and particularly it highlights the attention on the following five important input variables: pre-consolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL), and natural water content (W). These are selected in view of their well-understood impact on the USS. This study reports an innovative effort to use SHO and AOSM for the model's hyperparameter tuning, reducing heuristic methods and computationally expensive brute-force searches. This will provide a neat methodology for improving accuracy in USS predictions and maintaining the optimality of the model performance. The results, therefore, provide geotechnical engineers and researchers with considerable benefits. They give a sound basis that is data-driven for the assessment of USS in soft sensitive clays and advance the safety and stability of civil engineering projects. |
| format | Article |
| id | doaj-art-ad5cd92ead7b400f93811c6ba5b34aa5 |
| institution | Kabale University |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-ad5cd92ead7b400f93811c6ba5b34aa52025-02-02T12:26:06ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122025-02-0172112110.1186/s44147-025-00586-zEnhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modelingChen Fang0Ying Li1Yang Shi2School of Civil Engineering, Yancheng Institute of TechnologyThe United Graduate School of Agricultural Science, Gifu UniversityJiangsu Coastal Area Institute of Agricultural SciencesAbstract In geotechnical engineering, it is crucial to make sure that the undrained shear strength (USS) of soft, sensitive clays is accurately assessed. The accuracy in forecasting USS is pivotal for ensuring the structural integrity and stability of foundations and earthworks. Addressing this concern, advanced data-driven NB techniques are utilized to disclose the complex interactions of USS with basic soil parameters. This paper presents a novel methodology for the USS prediction in soft clays using machine learning techniques, and particularly it highlights the attention on the following five important input variables: pre-consolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL), and natural water content (W). These are selected in view of their well-understood impact on the USS. This study reports an innovative effort to use SHO and AOSM for the model's hyperparameter tuning, reducing heuristic methods and computationally expensive brute-force searches. This will provide a neat methodology for improving accuracy in USS predictions and maintaining the optimality of the model performance. The results, therefore, provide geotechnical engineers and researchers with considerable benefits. They give a sound basis that is data-driven for the assessment of USS in soft sensitive clays and advance the safety and stability of civil engineering projects.https://doi.org/10.1186/s44147-025-00586-zUndrained shear strengthNaïve BayesSea horse optimizerAdaptive opposition slime mould algorithm |
| spellingShingle | Chen Fang Ying Li Yang Shi Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling Journal of Engineering and Applied Science Undrained shear strength Naïve Bayes Sea horse optimizer Adaptive opposition slime mould algorithm |
| title | Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling |
| title_full | Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling |
| title_fullStr | Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling |
| title_full_unstemmed | Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling |
| title_short | Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling |
| title_sort | enhancing undrained shear strength prediction a robust hybrid machine learning approach with naive bayes modeling |
| topic | Undrained shear strength Naïve Bayes Sea horse optimizer Adaptive opposition slime mould algorithm |
| url | https://doi.org/10.1186/s44147-025-00586-z |
| work_keys_str_mv | AT chenfang enhancingundrainedshearstrengthpredictionarobusthybridmachinelearningapproachwithnaivebayesmodeling AT yingli enhancingundrainedshearstrengthpredictionarobusthybridmachinelearningapproachwithnaivebayesmodeling AT yangshi enhancingundrainedshearstrengthpredictionarobusthybridmachinelearningapproachwithnaivebayesmodeling |