## 3 Conclusions  
The papers collated in this Special Issue encompass the applications of machine learning methods across almost all geotechnical engineering disciplines, including underground and foundation engineering and cover a variety of stages, including planning and design, construction, and operation. This indicates that machine learning can be used across all stages of geotechnical engineering--that is, its applications can cover the full geotechnical engineering lifecycle. Moreover, machine learning methods can prove useful in the field of geomechanics, e.g., in the evaluation of the physical and mechanical parameters of geomaterials (including soil and rock). In addition, this collection covers numerous machine learning methods, including regression methods (Gaussian process regression, regression trees, ensembles of regression trees, support vector machine, reduced-order model, ran dom forest, extreme gradient boosting, light gradient boosting machine, artificial neural network, decision tree, multivariate adaptive regression splines model, k-nearest neighbors regression, adaptive boosting regression, and CatBoost algorithm); intelligent optimization methods, from swarm intelligence and evolutionary algorithms to nature-inspired computation (differential evolution algorithm, grey wolf optimization, backtracking search optimization algorithm, multi-verse optimizer, golden eagle optimizer, poor and rich optimization algorithm, Bayesian optimization algorithm, non-dominated sorting genetic algorithm (NSGA-II), particle swarm optimization, genetic algorithm, and whale optimization algorithm); and even some deep learning methods (two-dimensional convolutional neural network, long short-term memory recurrent neural network, and deep belief network). Many of the studies presented here construct data-driven models with the aid of machine learning methods. For these studies, the key is to obtain a suitable dataset. Various methods are employed to achieve this goal, including field tests, laboratory tests, numerical simulation, previous studies, etc. Some of those datasets can be called big data. In terms of the subjects covered by this Special Issue, the application of machine learning methods in geomechanics emerges as the main discussion topic, with eight papers. In seven of these, the research object is obtaining the properties of soil, as this can easily be achieved by tests. Of those seven papers, three focus on estimating mechanical parameters, another three on estimating physical parameters, and only one on soil classification. The final paper in this group of eight analyzes the mechanical parameters of rock. In these studies, information about the property parameters of geomaterials generally comes from laboratory tests. The next main topic in this Special Issue is the application of machine learning methods in underground engineering, and there are six papers in this field. Among them, the most popular research object is the construction stage of engineering, which is the focus of four papers. Three of these are on the back analysis of tunnels and one on ground subsidence risks for underground utilities. Engineering during the design stage is the research object of one paper, which determines shield tunneling parameters. Finally, there is one paper whose research object is engineering during the operation period. It analyzes utility tunnel performance in soft foundations affected by operation factors. The third major topic in this Special Issue is the application of machine learning methods in foundation engineering, which is discussed in four papers. The majority are concerned with engineering during the construction stage, with three papers covering three different research objects--the undrained bearing capacity of skirted foundations, the interaction between soil and structure, and the dynamic response of rock foundations. In the remaining paper, the research object is engineering during the design period. In this study, the optimization of a foundation pit dewatering design is conducted.