## 2 An Overview of the Published Papers  
In the study by [PERSON] et al. (contribution 1), the undrained shear strength of sensitive alluvial soft clay located in northern areas of the Nile River, Egypt, is determined using the machine learning approach. This analysis is key to assessing the stability of foundation engineering in this type of soil. Generally, the main method to determine the undrained shear strength of soil is through laboratory tests. However, this is costly and time-consuming. Moreover, extracting undisturbed samples of sensitive clay from construction sites it extremely difficult. Therefore, based on a dataset of 111 geotechnical testing points from laboratory and field tests, [PERSON] et al. use several machine learning models (including linear regression, Gaussian process regression, regression trees, ensembles of regression trees, and support vector machine) to determine the undrained shear strength of sensitive alluvial soft clay according to the soil's key features, which include water content, liquid limit, dry unit weight, plasticity index, consistency index, void ratio, specific gravity, and pocket penetration shear. The performance of each machine learning model is assessed through its coefficient of determination. The results show that the most accurate model is the support vector regression model. The study by [PERSON] et al. (contribution 2) is focused on the back analysis of surrounding rock parameters of underground engineering. For the construction of large-span arch cover metro stations, the determination of the surrounding rock parameters is very important. In this paper, to obtain the surrounding rock parameters of Shikui Road station, located on Dalian Metro Line Five, an intelligent back analysis method, called the Gaussian process differential evolution co-optimization algorithm (GP-DE algorithm), is proposed. In this method, based on the data obtained by the FLAC3D finite element model using the orthogonal scheme for numerical results, the relationship between monitoring data (displacement and stress) and the surrounding rock parameters is constructed by the Gaussian process model. Based on data measured in real-life engineering, through the optimization of the differential evolution algorithm, suitable surrounding rock parameters can be obtained. Finally, by using the forward calculation of FLAC3D, the accuracy of the inversion parameters is verified. The results show that the forward calculation results obtained using the inversed parameters are in good agreement with those obtained in real life, and the accuracy of this back analysis method is very high. The third article in this Special Issue is also focused on the back analysis of geomaterial mechanical parameters of underground engineering. In this study, a novel back analysis method combining a reduced-order model (ROM) and grey wolf optimization (GWO) has been proposed. In this method, the ROM is adopted to construct a surrogated model between the response of the underground structure and the surrounding rock parameters based on a numerical model, and GWO is used to optimize the surrounding rock parameters based on the ROM. The proposed method is applied to determine the surrounding rock parameters of two tunnels. The results show that the obtained surrounding rock parameters are in excellent agreement with the actual parameters, and the deformation results computed based on those parameters are consistent with the theoretical deformation results. In the fourth article in this collection, the synthetic aperture radar (SAR) and optical image registration are studied. The SAR and optical images collect rich spectral information for ground objects, but their qualities are seriously affected by atmospheric attenuation and weather conditions. Therefore, improving the quality of these images is crucial to improving geotechnical engineering surveys. In this study, a novel method for SAR and optical image registration is proposed.