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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]

Preciously assessing the creep mechanical response of sand–geomembrane interfaces is vital for the design of relevant engineering applications, which is inevitable to be influenced by temperature and stress statuses. In this paper, based on the self-developed temperature-controlled large interface shear apparatus, a series of long-term creep shear tests on textured geomembrane–silica sand interfaces in different temperatures, normal pressure, and creep shear pressure were conducted, and a database compiled from the physical creep shear test results is constructed. By adopting the database, three disparate machine learning algorithms of the Back Propagation Artificial Neural Network (BPANN), the Support Vector Machine (SVM) and the Extreme Learning Machine (ELM) were adopted to assess the long-term creep mechanical properties of sand–geomembrane interfaces while also considering the influence of temperature. Then, the forecasting results of the different algorithms was compared and analyzed. Furthermore, by using the optimal machine learning model, sensitivity analysis was carried out. The research indicated that the BPANN model has the best forecasting performance according to the statistics criteria of the Root-Mean-Square Error, the Correlation Coefficient, Wilmot's Index of Agreement, and the Mean Absolute Percentage Error among the developed models. Temperature is the most important influence factor on the creep interface mechanical properties, followed with time. The research findings can support the operating safety of the related engineering facilities installed with the geomembrane. [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]

The presence of internal fissures holds immense sway over the gas permeability of sustainable cement mortar, which in turn dictates the longevity and steadfastness of associated edifices. Nevertheless, predicting the gas permeability of sustainable cement mortar that contains internal cracks poses a significant challenge due to the presence of numerous influential variables and intricate interdependent mechanisms. To solve the deficiency, this research establishes an innovative machine learning algorithm via the integration of the Mind Evolutionary Algorithm (MEA) with the Adaptive Boosting Algorithm-Back Propagation Artificial Neural Network (ABA-BPANN) ensemble algorithm to predict the gas permeability of sustainable cement mortar that contains internal cracks, based on the results of 1452 gas permeability tests. Firstly, the present study employs the MEA-tuned ABA-BPANN model as the primary tool for gas permeability prediction in cement mortar, a comparative analysis is conducted with conventional machine learning models such as Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) optimised ABA-BPANN, MEA optimised Extreme Learning Machine (ELM), and BPANN. The efficacy of the MEA-tuned ABA-BPANN model is verified, thereby demonstrating its proficiency. In addition, the sensitivity analysis conducted with the aid of the innovative model has revealed that the gas permeability of durable cement mortar incorporating internal cracks is more profoundly affected by the dimensions and quantities of such cracks than by the stress conditions to which the mortar is subjected. Thirdly, puts forth a novel machine-learning model, which enables the establishment of an analytical formula for the precise prediction of gas permeability. This formula can be employed by individuals who lack familiarity with machine learning skills. The proposed model, namely the MEA-optimised ABA-BPANN algorithm, exhibits significant potential in accurately estimating the gas permeability of sustainable cement mortar that contains internal cracks in varying stress environments. The study highlights the algorithm's ability to offer essential insights for designing related structures. [ABSTRACT FROM AUTHOR]

The paper proposes a method to simulate the mechanical behavior of compact rock considering hydromechanics by combining physical experiments and numerical analysis. The effectiveness of the constructed method is validated by the comparison between the numerical and physical results of triaxial shear experiments on sandstone in seepage conditions. Based on the validated method, the stability of underground water-sealed oil and gas storage caverns in surrounding compact sandstone during excavation is analyzed. The main findings are as follows: The intrinsic permeability of compact sandstone has a power function relationship with the porosity; the combination of the porous media elastic model and the modified Drucker–Prager plasticity model can preciously represent the mechanical properties of compact sandstone; the proposed method can accurately replicate the hydromechanical response of compact sandstone in seepage conditions; the effects of hydromechanical effects have significant impacts on the stability of surround compact sandstone during the excavation of underground water sealed oil and gas storage caverns, which causes the obvious increase in stress, deformation and plastic deformation zones of the surrounding compact sandstone and remarkable decrease in the stability safety factor. [ABSTRACT FROM AUTHOR]