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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]

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 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]