ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Solutions
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1630481
This article is part of the Research TopicBig Data and AI for Sustainable Maritime OperationsView all 6 articles
Predictive modeling on the mechanical properties of Marine Coral Sand-Clay Mixtures based on Machine Learning Algorithms and Triaxial shear Tests
Provisionally accepted
- 1Shanxi Ningguli New Materials Joint Stock Company Limited, jinzhong, China
- 2Shanghai Maritime University, Shanghai, China
- 3Nanjing Forestry University, Nanjing, China
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Abstract：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.
Keywords: Marine coral sand-clay mixture, Strength prediction, LDA-BPNN model, machine learning, empirical formula
Received: 17 May 2025; Accepted: 08 Aug 2025.
Copyright: © 2025 Yang, Xu, Liu, Chao and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhiming Chao, Shanghai Maritime University, Shanghai, China
Peng Cui, Nanjing Forestry University, Nanjing, China
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