Temperature-Dependent Post-Cyclic Mechanical Characteristics of Interfaces between Geogrid and Marine Reef Sand: Experimental Research and Machine Learning Modeling

Author:

Chao Zhiming12,Wang Haoyu2,Zheng Jinhai1,Shi Danda2,Li Chunxu34,Ding Gege3,Feng Xianhui5

Affiliation:

1. Institute of Water Sciences and Technology, Hohai University, Nanjing 211106, China

2. College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 200135, China

3. China Waterborne Transport Research Institute, Beijing 100088, China

4. School of Information, Renmin University of China, Beijing 100872, China

5. School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract

The mechanical response of the marine reef sand–geogrid (RG) interface can be influenced by a high-temperature climate, grain size, and variable stress environments. These factors are critical to the effectiveness of geogrid reinforcement in reef sand engineering. However, there are few studies on the influences of grain size, temperature, and stress history on the mechanical characteristics of RG interfaces, with most studies centering on the influence of single factors on the mechanical characteristics of RG interfaces. In this paper, based on self-developed temperature-controlled large interface shear equipment, a series of before/post-cyclic shear tests were carried out on RG interfaces in the temperature range of 5–80 °C. The impact of different reef sand grain sizes on the RG interface was explored (S1: 1–2 mm; S2: 2–4 mm). It was shown that temperature and grain size had significant influences on the mechanical characteristics of the RS interface. Compared with the S1 RG interfaces, the S2 RG interfaces had higher sensitivity to temperature changes with respect to the before/post-cyclic maximum shear strength. Moreover, in comparison to the before-cyclic shear strength, the post-cyclic maximum shear strength is more responsive to temperature changes. The before/post-cyclic maximum shear strength of the S2 RG interfaces was greater than the maximum shear strength of the S1 RG interfaces as the temperature changed. Based on the results of physical tests, a machine learning model containing 450 datasets was constructed, which can accurately predict the shear strength of the RG interface.

Funder

National Natural Science Foundation of China Youth Science Fund Program

Failure Mechanics and Engineering Disaster Prevention, Key Lab of Sichuan Province

Shanghai Sailing Program

Shanghai Natural Science Foundation

China Postdoctoral Science Foundation

The Shanghai Soft Science Key Project

Key Laboratory of the Ministry of Education for Coastal Disaster and Protection, Hohai University

Key Laboratory of Estuarine and Coastal Engineering, Ministry of Transport

The National Key Research and Development Program of China

9th Youth Talent Lifting Project of China Association for Science and Technology

The Central Level Research Institutes Research Special Projects

Publisher

MDPI AG

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