## 1 Introduction  
Geotechnical engineering is civil engineering constructed in rock and soil and includes three main types: underground, foundation, and slope engineering. Because rock and soil are typical natural geological bodies, their mechanical properties and internal structures are very complex [1]. They are characterized by complicated physical, mechanical, and chemical behaviors, and their structure is a three-phased system which changes depending on water content and environmental conditions in a highly non-linear manner. Moreover, rock and soil are anisotropic and heterogeneous by nature owing to differences in their origins and formation processes. Therefore, most geotechnical engineering problems involve the coupling of multiple fields and multiple phases; generally, they cannot be solved easily. Moreover, unsafe geotechnical engineering will bring about serious disasters, such as landslides and surface subsidence, which cannot be solved well by traditional (e.g., theoretical, numerical, and experimental) methods. However, the development of artificial intelligence has supported the development of better solutions to geotechnical engineering problems. Machine learning methods have been applied widely to this end and have gained prominence in current research trends [2, 3]. The first study to use machine learning in geotechnical engineering was conducted by Stanford in 1983 [4] and addressed the potential applications of expert systems in the domain of slope engineering specifically. From that moment on, machine learning methods have been gaining increasingly widespread use in the field of geotechnical engineering. Because geomechanics are the foundation of geotechnical engineering, nowadays, there are numerous studies on the applications of machine learning methods in geomechanics. These include two main aspects: geotechnical parameters and geotechnical constitutive models [5, 6]. Numerous studies have also explored using machine learning methods in the three main types of geotechnical engineering: underground engineering [7, 8], foundation engineering [9], and slope engineering [10]. Moreover, machine learning methods have been used to solve specific geotechnical engineering problems, including soil-structure interactions [11] and seepage of dams [12]. In addition, machine learning methods have been employed to predict geotechnical engineering disasters, such as ground surface settlements [13], landslides [14], rock bursts [15], etc. Currently, machine learning methods have become prevalent in the development of boring machines for underground engineering: the construction of integrated management systems for tunnel boring machine operations [16], the safety prediction of shield tunnel construction [17], the prediction of shield attitudes [18], and main drive torque estimation in shield tunnelling [19] are some of the areas in which they have proven useful. Therefore, nowadays, machine learning methods have been applied in most, if not all, fields of geotechnical engineering and geomechanics. Almost all types of machine learning methods have been employed in this field; examples include expert systems, fuzzy systems, artificial neural networks, deep learning methods, swarm intelligence, evolutionary algorithms, big data analysis, biological computation, nature-inspired computation, support vector machine, and Gaussian process regression. Out of these, the use of artificial neural networks has been the most extensive.  
This Special Issue presents new applications of machine learning methods in the field of geotechnical engineering, from planning and design to construction. It contains 19 articles, which I will briefly describe in the next section. It is not the purpose of this Editorial to elaborate on each of the papers, but rather to encourage the reader to explore them on their own.