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- 1Shanghai Maritime University, Shanghai, China
- 2Umea Universitet, Umeå, Sweden
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.