## 3 Conclusions
The last topic in this Special Issue is the application of machine learning methods in the planning of geotechnical engineering, with only one paper concentrating on this area. This paper focuses on the identification of geophysical prospecting information. From the above analysis, it is evident that the geomechanics subjects in this compilation of articles are almost exclusively the parameters of geomaterials, and that one main geomechanics subject is lacking, namely the constitutive model of geomaterials. Moreover, in terms of geotechnical engineering subjects, a major area, namely slope engineering, is equally missing from this Special Issue. On top of the subjects mentioned above, machine learning methods can also be applied in the study of engineering disasters related to geotechnical engineering. Therefore, the papers in this Special Issue are only some of the new developments in the application of machine learning in geotechnical engineering, and should be seen not only as new results, but also as starting points, inviting readers to conduct future studies on the themes explored here. As a final note, I would like to highlight the particularity that all papers in this Special Issue are on the construction of data-driven models, which is the main application of machine learning methods in geotechnical engineering. However, datasets obtained in geotechnical engineering, no matter what method is used (including field tests, laboratory tests, and numerical simulations), are not complete datasets, and their number is very limited; that is, these datasets only describe very limited, partial properties of real engineering. Therefore, purely data-driven models will run into essential problems. However, the properties of real engineering can partly be described by mechanical models on the soil and rock mechanics. Therefore, in order to apply machine learning in geotechnical engineering, mechanical models should be included as well, as is the case in physics-based machine learning [20]. Moreover, to obtain more big datasets, the development of modern monitoring technology is another way that will promote the application of machine learning in geotechnical engineering. The author declares no conflicts of interest. List of Contributions:  
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