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ORIGINAL RESEARCH article

Front. Mar. Sci.

Sec. Ocean Solutions

Volume 12 - 2025 | doi: 10.3389/fmars.2025.1653741

Strength Estimation of Textured Polymer Layer-Reinforced Materials in Practical Marine Engineering Based on Physical Experiments and Artificial Intelligence Modelling

Provisionally accepted
Danda  ShiDanda Shi1Kaiwei  XuKaiwei Xu1Xin  YuXin Yu1Zhiming  ChaoZhiming Chao1Peng  CuiPeng Cui2*
  • 1Shanghai Maritime University, Shanghai, China
  • 2Umea Universitet, Umeå, Sweden

The final, formatted version of the article will be published soon.

Marine coral sand-clay mixtures (MCCM) are widely used as fill materials in ocean engineering, where their strength is influenced by marine clay content. This study investigates the mechanical behavior of textured polymer layer-reinforced MCCM using 3D-printed technology with varying asperity heights, spacings, and reinforcement layers. Triaxial tests reveal that increased reinforcement, higher asperities, and smaller spacings enhance strength and internal friction angle with minimal effect on cohesion.Particle breakage increases with reinforcement, and fractal analysis shows a linear relationship between fractal dimension and breakage rate. SEM images reveal the complex interfacial interaction mechanisms between the MCCM and the polymer layer.A comprehensive dataset from these tests supports the development of predictive models, including BPNN, GA-BPNN, PSO-BPNN, and LDA-BPNN, with the LDA-BPNN showing the highest accuracy and generalization. Compared with existing approaches, the proposed model framework achieves significant improvements in predictive performance and robustness. Sensitivity analysis identifies asperity spacing and asperity height as key factors. An empirical formula derived from the LDA-BPNN enables practical strength prediction, offering valuable guidance for marine construction design.

Keywords: Textured polymer layer reinforcement, Marine coral sand-clay mixture, 3D printing technology, Triaxial shear tests, machine learning

Received: 25 Jun 2025; Accepted: 17 Jul 2025.

Copyright: © 2025 Shi, Xu, Yu, 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: Peng Cui, Umea Universitet, Umeå, Sweden

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