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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/14/2481 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2075-5309 |