## 2 An Overview of the Published Papers
The study by [PERSON] and [PERSON] (contribution 12) is also focused on the prediction of soil behavior using machine learning methods. In this study, based on a dataset from 1068 tests on sand (including microscopy, direct shear, oedometer, and specific gravity tests), a machine learning model for evaluating the influence of sand particle morphology on shear strength is constructed. Two machine learning methods--multiple linear regression and random forest regression--are applied and compared. The features of sand particle morphology considered by the two machine learning models include mean particle size, uniformity coefficient, curvature coefficient, dry density, normal stress, and particle regularity. The results show that the prediction accuracy of both models is very high compared to the experimental results, and the most important factor affecting the shear strength of sand is mean particle size. The next study, by [PERSON] and [PERSON] (contribution 13), is focused on the prediction of soil shear wave velocity, which is an essential parameter in evaluating the seismic response in foundation engineering. In this study, based on a collected dataset with 1000 cone penetration test data, four machine learning methods (random forest, support vector machine, decision trees, and extreme gradient boosting) are employed to predict soil shear wave velocity according to four features: cone tip resistance, sleeve friction, friction ratio, and soil depth. Moreover, to improve the performance of the four machine learning methods, their hyperparameters are optimized using Bayesian optimization with the k-fold cross-validation method. Eight metrics (root-mean-square error, mean absolute error, mean absolute percentage error, coefficient of determination, performance index, scatter index, uncertainty analysis at 95% confidence level, and proportion of samples that fall within \(\pm\)10% deviation from the predicted values compared with the target value) are used to evaluate the performance of the proposed machine learning methods. The results show that the performance of the random forest is the best, achieving the highest accuracy and the lowest level of errors. The study by [PERSON] et al. (contribution 14) focuses on the application of the multi-objective optimization method for the optimization of the foundation pit dewatering scheme, which is very important for the safety of foundation engineering. Using the foundation pit dewatering theory and the multi-objective optimization algorithm of the non-dominated sorting genetic algorithm (NSGA-II), the authors optimize the foundation pit dewatering scheme for a foundation pit dewatering project at an inverted siphon section of the Xixiayuan canal head. This multi-objective optimization has three objectives, which are minimum total cost of dewatering, minimum amount of land subsidence caused by dewatering, and maximum drawdown of water level in the center of the foundation pit. Using this new method, a Pareto-optimal solution set with uniform distribution is obtained, and the optimization scheme is applied to the solution set to produce multiple feasible schemes for real dewatering. The results show that the obtained foundation pit dewatering scheme meets the requirements for water level and settlement control. The article by [PERSON] et al. (contribution 15) focuses on the effects of dry density and moisture content on kaolin-brass interfacial shear adhesion. In this study, to evaluate the interface shear adhesion between compacted kaolin clay and a metallic surface, a new testing method is proposed. Compacted kaolin clay specimens with various energy levels and moisture contents are used to determine the interface shear adhesion strength between reconstituted kaolin clay and a metallic surface. The results show that to provide the highest density and divide the compaction curve into dry and wet sides, the optimum moisture content is 30%, and as the clay's dry density increases, the interface shear adhesion strength increases too.