Predictive modeling on the mechanical properties of marine coral sand-clay mixtures based on machine learning algorithms and triaxial shear tests

Marine coral sand-clay mixtures (MCCM) are widely used as fill materials in offshore engineering, where their strength characteristics are critical to structural stability and safety. This study conducted a series of triaxial shear tests under varying conditions of clay content, reinforcement layers, confining pressure, water content, and strain to establish a comprehensive strength database for MCCM. Based on this dataset, multiple predictive models were developed, including Backpropagation Neural Network (BPNN), Genetic Algorithm optimized BPNN (GA-BPNN), Particle Swarm Optimization enhanced BPNN (PSO-BPNN), and a Logical Development Algorithm preprocessed BPNN model (LDA-BPNN). Among them, the LDA-BPNN model demonstrated superior accuracy and generalization capabilities compared to traditional optimization algorithms. Sensitivity analysis identified water content, clay content, and confining pressure as the primary factors influencing MCCM strength. Furthermore, an explicit empirical formula derived from the LDA-BPNN model was proposed, offering a practical and efficient tool for engineers without specialized machine learning expertise. These findings provide valuable technical support for the optimized design and safety assessment of MCCM materials in marine geotechnical engineering applications.