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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]

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 peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. • The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared. • An analytical equation for estimating the peak shear strength is proposed. • Physical experiments are conducted to validate the effectiveness of the proposed analytical equation. [ABSTRACT FROM AUTHOR]

In this paper, to investigate the impact of environmental loadings on the short-term and creep mechanical characteristics of different types of clayey soil-Geocomposite Drainage Layers (GDL) interfaces, a series of rapid loading and creep shear tests were conducted on Mercia Mudstone Clay-GDL interfaces and Kaolin Clay-GDL interfaces subjected to drying-wetting cycles, thermal cycles and elevated temperature, etc, using a bespoke temperature and stress-controlled large direct shear apparatus. The experimental results indicate that, compared with the original specimens, the interfaces subjected to drying-wetting cycles, thermal cycles and elevated temperature, have lower peak shear strength and creep shear resistance. For example, under 25 kPa normal stress, the peak shear strength of original Mercia Mudstone Clay-GDL interfaces and Kaolin Clay-GDL interfaces falls by 11.91% and 10.11%, respectively, when subjected to 1 drying-wetting cycle. This can be ascribed to the weakening of interlocking effects and skin friction between soil and GDL caused by the softening of drainage core and geotextile fibres of GDL. The peak shear strength of clayey soil-GDL interfaces subjected to one drying-wetting cycle is lower than that subjected to one thermal cycle because of the reduction in the peak shear strength of clayey soil above GDL during drying-wetting cycles. The impact of drying alone on the decrease in the peak shear strength of clayey soil-GDL interfaces during drying cycles with heating is small, and the main influence factor is the elevated temperature. • Rapid loading direct shear tests and creep shear tests on clayey soil -GDL interfaces subjected to environmental loadings were performed. • In-depth understanding of the mechanical responses of clayey soil-GDL interfaces under environmental loadings is demonstrated. • The impacts of environmental factors on the short-term mechanical characteristics of clayey soil-GDL interfaces are analysed. • The impacts of environmental factors on the creep mechanical characteristics of clayey soil-GDL interfaces are analysed. [ABSTRACT FROM AUTHOR]

To widely apply coral sand aggregate (CSA) concrete in practical ocean engineering, it requires to have a precious evaluation of its compressive strength in marine environment. In the study, 1280 triaxial shear tests on concrete containing different content and properties of CSA in marine environment was conducted. Based on the test results, a database was established, and a unique machine learning method was developed via combining the Logic Development Algorithm (LDA) with the ensemble technique of Adaptive Boosting Algorithm (AdaBoost) and Artificial Neural Network (ANN). This novel approach represents the first attempt to utilize this model for estimating the compressive strength of CSA concrete. To validate the applicability of the proposed approach, five conventional machine learning algorithms were also built as a reference, including LDA optimized ANN and Support Vector Machine, Particle Swarm Optimization Algorithm and Genetic Algorithm optimized AdaBoost-ANN. The research findings indicate: Firstly, the AdaBoost-ANN model optimized by LDA outperforms than the conventional models in terms of predictive accuracy and efficiency; For example, for the LDA-AdaBoost-ANN, LDA-SVM, LDA-ANN, GA-AdaBoost-ANN and PSO-AdaBoost-ANN model, their Root Mean Square Error (RMSE) is 2.73, 5.82, 6.97, 7.47 and 3.85 on testing datasets respectively; for the LDA-AdaBoost-ANN, LDA-SVM, LDA-ANN, GA-AdaBoost-ANN and PSO-AdaBoost-ANN model, their Mean Absolute Error (MAE) is 4.8, 11.34, 12.5, 9.86 and 8.45 on testing datasets respectively; for the LDA-AdaBoost-ANN, LDA-SVM, LDA-ANN, GA-AdaBoost-ANN and PSO-AdaBoost-ANN model, their Mean Absolute Percentage Error (MAPE) is 6.26%, 23.36%, 24.35%, 10.19% and 13.79% on testing datasets respectively. Secondly, the sensitivity analysis by using the novel model reveals that confining pressure, CSA content and immersion period in seawater have relatively high impact on the compressive strength. Thirdly, an analytical formula was established based on the novel algorithm. • 1280 triaxial shear tests were performed on coral sand aggregate concrete. • A unique machine learning model was developed based on the experimental database. • The compressive strength of the concrete was assessed by using the model. [ABSTRACT FROM AUTHOR]

The high-temperature climate, dynamic stress loading and soil particle dimension have non-negligible influence on the interface interaction between marine coral sand and polymer layer, which determines the stability of coral sand engineering facilities installed with polymers. However, currently, the relevant research is rare. In this paper, by using the self-developed large temperature-controlled interface dynamic shear apparatus, a series of cyclic shear tests were conducted on the interfaces between polymer layer and coral sand with the particle size range of 1 mm ∼ 2 mm (S1 coral sand) and 2 mm ∼ 4 mm (S2 coral sand) in the temperature ranging from 5 °C to 80 °C. The experimental results indicate that, from 5 °C to 60 °C, the peak shear strength, dynamic shear stiffness and damping ratio rise, while from 60 °C to 80 °C, the decline of the mechanical parameters occurs. Also, temperature has more significant influence on the dynamic mechanical properties of S1 coral sand interfaces than that of S2 coral sand interfaces. Additionally, except for 60 °C, the peak shear strength, dynamic shear stiffness and damping ratio of S1 coral sand interfaces is all higher than that of S2 coral sand interfaces in other test temperature. • A series of temperature-controlled cyclic shear tests were conducted on coral sand interfaces. • The influence of temperature on the dynamic interface interaction was investigated. • The influence of soil particle size on the dynamic interface interaction was investigated. [ABSTRACT FROM AUTHOR]

In this paper, a bespoke stress and temperature controlled direct shear apparatus to test soil-geosynthetics interfaces is introduced. By adopting the apparatus, a series of 'rapid loading' shear tests and creep tests were conducted on the Clay – Geosynthetic Drainage layer (GDL) interfaces to assess the functionality of the apparatus. The experimental results indicate that, the modified apparatus can allow the shear deformation behaviour of soil-geosynthetics interfaces under environmental stress during thermal and drying-wetting cycles to be investigated, with a reliable performance. The resistance of Clay-GDL interfaces to shear deformation under the rapid loading of shear stress decreases after drying-wetting cycle and at elevated temperature. In the creep tests, the interfaces subjected to drying-wetting cycles and thermal cycles fail under a lower shear stress level than that of the interfaces without experiencing drying-wetting cycles and thermal cycles, respectively. The impacts of drying cycles on the horizontal displacement is significantly larger than that of wetting cycles. The first drying cycle has the largest impacts on the horizontal displacement than those of the following drying cycles. The impacts of drying alone on the horizontal displacement of Clay-GDL interfaces during drying cycles are small, and the main influence factor is the elevated temperature. • A bespoke stress and temperature controlled direct shear apparatus on soil-geosynthetics interfaces is introduced. • The performance of the modified apparatus was validated by carrying out repetitive tests. • The shear resistance of Clay-Geosynthetic Drainage Layer interfaces decreases after drying-wetting cycle. • The impacts of drying cycle on the creep horizontal displacement is significantly larger than that of wetting cycle. [ABSTRACT FROM AUTHOR]

The plastic waste has huge potential being used as the aggregate of concrete but the premise requires to have a sound understanding about the seepage mechanical properties of the light-weight concrete. In this study, initially, the physical experiment was conducted to measure the permeability and porosity of light-weight concrete with various amount of plastic waste aggregate (PWA) in different confining pressure and pore pressure. Based on the test results, a database was established. On this basis, a unique machine learning method was developed via combining the Logic Development Algorithm (LDA) with the ensemble technique of Adaptive Boosting Algorithm (BA) and Artificial Neural Network (ANN), which is the first attempt to utilize this model for estimating the permeability of PWA cement mortar. The research outcomes indicate, the corrected permeability and porosity of PWA cement mortar rises with the increase of PWA content, and the changing magnitude is more significant in high confining pressure; The corrected permeability and porosity of cement mortar with a higher content of PWA is more sensitive to the variation of confining pressure; The BA-ANN model optimised by LDA outperforms than the conventional machine learning models in terms of predictive accuracy and efficiency when assessing the permeability of PWA cement mortar; The sensitivity analysis by using the novel model reveals that confining pressure, PWA content and porosity have a relatively high impact on the permeability of PWA cement mortar, while the impact of pore pressure is relatively small. The study findings can provide guidance for the permeability and porosity design of relevant PWA concrete engineering facilities. • Permeability and porosity tests on plastic waste aggregate concrete were conducted. • A unique machine learning method was developed based on the experimental results. • Permeability of the concrete was preciously estimated by using the developed model. [ABSTRACT FROM AUTHOR]

In this paper, the permeability and porosity of self-prepared artificial columnar jointed rock masses (CJRM) with different columnar dip angles under different pore pressures during cyclic loading-unloading of confining pressure were measured. The experimental results indicate that the gas permeability of the artificial CJRM gradually decreases with the rise of pore pressure due to the existence of Klinkenberg effect, and Klinkenberg effect gradually decreases with the rise of columnar dip angle. The existence of it improves significantly the intrinsic permeability. Besides, the intrinsic permeability and porosity of artificial CJRM are more sensitive to the first loading than that of the intact cement mortar specimen. The permanent deformation of pores in the specimens mainly appears in the first cycle, and the subsequent cycles have smaller effects on the intrinsic permeability and porosity. The permanent deformation results in the intrinsic permeability, and porosity in the loading process is always higher than those in the unloading process. The intrinsic permeability gradually increases with the growth of columnar dip angle, exhibiting obvious permeability anisotropy. Under high confining pressure, the permeability anisotropy of artificial CJRM is less than that under low confining pressure. Meanwhile, under the same confining pressure, during the repeated loading-unloading cycles, the permeability anisotropy decreases gradually. The existence of a representative element volume (REV) for CJRM is verified; thus, the equivalent continuum media model can be utilized to analyse the seepage characteristics of CJRM. [ABSTRACT FROM AUTHOR]

In this paper, a method is proposed to prepare rock-like materials with different thicknesses of hidden joints. Then, permeability and porosity of the self-prepared jointed specimens under different pore pressures during confining pressure loading and unloading are measured. The experimental results indicate that the gas permeability of the jointed specimens gradually decreases with the rise of pore pressure due to the existence of Klinkenberg effect, and Klinkenberg effect gradually decreases with the rise of hidden joint thickness. As the main seepage channels, hidden joints govern the seepage characteristics, and due to the existence of hidden joints, the intrinsic permeability is improved significantly. Besides, due to the existence of hidden joints, the intrinsic permeability and porosity are more sensitive to confining pressure loading than that of the intact specimen, and the sensitivity increases with the rise of hidden joint thickness. During confining pressure loading, there is a permanent deformation of the hidden joints and pores in the specimens, which results in both the intrinsic permeability and porosity being always lower than those in the loading process. Meanwhile, the permanent deformation rises with the increases of hidden joint thickness, which leads to the increases of gap of intrinsic permeability and porosity under loading and unloading processes. Additionally, after comparison of the fitting results, the sub-cubic law can reflect the relationship between flow rate and the thickness of non-persistent joints better than the cubic law. [ABSTRACT FROM AUTHOR]

• A method for preparing cement mortar with diverse numbers of internal cracks was proposed. • Permeability and porosity for the samples under diverse stress states were measured. • The stress-dependent permeability and porosity for the prepared samples was analysed. In this paper, gas permeability and porosity for the prepared cement mortar samples with diverse numbers of internal cracks under diverse pore pressure during confining pressure loading and unloading was measured. According to the outcomes of experiments, the following findings were obtained: Klinkenberg correction is appliable to the modification on gas permeability of cement mortar samples containing different number of internal cracks to obtain the intrinsic permeability, with the quadratic equation (Coefficient of Determination R2 is above than 0.92) having a better performance than that of Klinkenberg equation; Owing to the increase in the equivalent pore radius of seepage passages, Klinkenberg Effect of the cement mortar samples reduces when the number of internal cracks rises. When the number of internal cracks rises from 0 to 12, the slippage factor decreases over 90 %; When the number of internal cracks is high, the same changing extent in the number of internal cracks can lead to higher influence on the intrinsic permeability than that when the number of internal cracks is low. The variation magnitude of intrinsic permeability when the number of internal cracks increases from 6 to 12 is around 3–10 times higher than that when the number goes up from 0 to 6; When confining pressure increases, the sensitivity of intrinsic permeability to the changes in confining pressure reduces. The variation magnitude of intrinsic permeability when confining pressure is loaded from 0 MPa to 25 MPa is 5–10 times higher than that when confining pressure is loaded from 25 MPa to 45 MPa; Owing to the permanent deformation of pores and cracks, under the same confining pressure, the intrinsic permeability and porosity during the unloading stage are less than those during the loading stage, respectively; The sub-cubic law (R2 is above than0.90) is able to describe gas seepage in cement mortar specimens with different number of internal cracks more precisely compared to the cubic law. [ABSTRACT FROM AUTHOR]

• A novel approach is used to obtain the relationships of the porosity and the permeability of low-permeability rock. • The logarithmic bulk modulus was obtained from the gas permeability experiments. • The tested parameters make the fluid-solid coupling simulation of low permeability sandstone a reality. • Numerical simulations of low-permeability sandstone are conducted based on the triaxial compression experiments. This paper proposed a novel approach, which combines an analytical model and a formula fitting function, to determine the hydromechanical relationships between the porosity and the permeability of low-permeability sandstone (LPS) that is further applied for the hydromechanical modelling. Then, several gas permeability experiments are conducted to measure the permeability and porosity of LPS under different confinement pressure. In conjunction with the Finite Element Method, the fracture propagation processes of LPS, which take physical triaxial experiments as reference experiments, are simulated by the considering hydromechanics. The results demonstrate that the absolute permeability is exponentially related to the porosity of LPS. Additionally, the porous media nonlinear elastic model and the modified Drucker–Prager cap plasticity model can accurately describe the deformation process of LPS. The simulation results correlate well with the experimental counterparts while being less scattered than the experimental data. This demonstrates the effectiveness of the proposed method in characterizing fracture properties of LPS considering hydromechanics. [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 selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture. [ABSTRACT FROM AUTHOR]

Monzonitic granite is a low-permeability rock. Monzonitic granite formations are ideal for underground storage of oil due to their low permeability and high mechanical strength. In this study, a series of coupled hydromechanical triaxial tests are carried out using monzonitic granite specimens. The influence of confining and fluid pressures on stress, strain, and permeability is investigated. Failure characteristics under different confining and fluid pressures are discussed based on the analysis of macro fracture planes and micro scanning electron microscopy (SEM). The test results show that the change of permeability with stress and strain reflects the deformation stages of compaction, compression, crack propagation, coalesce, and failure of cracks. Due to the low porosity, the change of permeability is small in the initial phases of compaction and compression, whereas there is a significant increase in permeability when new cracks start to develop and coalesce. Confining pressures have a significant impact on the strength and permeability, particularly the crack damage stress of the rock. Compared with confining pressure, the effect of fluid pressure on rock strength and crack damage stress is small. For the monzonitic granite specimens tested, changing the confining pressure results in different failure modes, whereas the fluid pressure has a relatively small effect on the failure modes. [ABSTRACT FROM AUTHOR]