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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. [ABSTRACT FROM AUTHOR]

With the development of data collection and storage capabilities in recent decades, abundant data have been accumulated in geotechnical engineering fields, providing opportunities for the usage of machine learning approaches. Thus, a rising number of scholars are adopting machine learning techniques to settle geotechnical issues. In this paper, the application of three popular machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), as well as other representative algorithms in geotechnical engineering, is reviewed. Meanwhile, the applicability of diverse machine learning algorithms in settling specific geotechnical engineering issues is compared. The main findings are as follows: ANN, SVM, and DT have been widely adopted to solve a variety of geotechnical engineering issues, such as the classification of soil and rock types, predicting the properties of geotechnical materials, etc. Based on the collected relevant research, the performance of random forest (RF) in sorting soil types and assessing landslide susceptibility is satisfying; SVM has high precision in classifying rock types and forecasting rock deformation; and backpropagation ANNs and Hopfield ANNs are recommended to forecast rock compressive strength and soil settlement, respectively. [ABSTRACT FROM AUTHOR]