Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Mar. Sci.

Sec. Ocean Solutions

Volume 12 - 2025 | doi: 10.3389/fmars.2025.1660611

Experimental Study on Mechanical Properties of Triaxial Geogrid Reinforced Marine coral sand-Clay Mixture Based on 3D Printing Technology

Provisionally accepted
Danda  ShiDanda Shi1Kaiwei  XuKaiwei Xu1Zhiming  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 in marine engineering, with their mechanical behavior strongly influenced by clay content. This study investigates the effects of 3D-printed triaxial geogrid reinforcement on MCCM through triaxial testing. Based on the experimental results, a dataset was established, while a novel machine learning model named GP-BPNN was proposed, integrating genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network (BPNN). This model was applied for the first time to predict the strength of MCCM. Results show that lower clay content, more reinforcement layers, and higher confining pressure significantly enhance the strength and cohesion of MCCM, with little effect on the internal friction angle. The strength first decreases, then increases, and finally decreases again with increasing water content. Particle breakage is influenced by clay content and water content; moreover, fractal analysis reveals a linear relationship between the breakage rate and the fractal dimension. SEM images reveal the interaction between MCCM and the geogrid. Additional stress and matrix suction analyses highlight the effects of reinforcement layers and water content on the strength. These findings offer insight into triaxial geogrid-reinforced MCCM behavior and provide guidance for marine engineering construction.

Keywords: triaxial geogrid reinforcement, Marine coral sand-clay mixture, 3D printing technology, Triaxial shear tests, Particle breakage, machine learning

Received: 06 Jul 2025; Accepted: 30 Jul 2025.

Copyright: © 2025 Shi, Xu, 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.