## 2 An Overview of the Published Papers
In the new method of feature point extraction, phase consistency intensity screening and scale space grid division are combined to obtain stable and uniform feature points from images, and in feature description, the extended phase consistency method is applied to calculate the gradient amplitude and direction of the images. The experimental results showed the superior matching performance of the new method compared with current state-of-the-art methods. The study by [PERSON] et al. (contribution 5) is also focused on the back analysis of surrounding rock parameters of underground engineering. In this study, based on data from a numerical model established based on a fluid-solid coupling finite element model, a surrogated model between the response of the surrounding rock (displacement and pore water pressure distribution) and surrounding rock parameters is constructed by the Gaussian process. According to the measured displacement and pore water pressure of the surrounding rock, this surrogated model searches for the suitable rock parameters using differential evolution. Therefore, the study proposes a new intelligent back analysis method for fluid-solid coupling of surrounding rock in tunnels in water-rich areas. Finally, this new back analysis method is verified by comparing the inversion parameters with the ones measured in real-life. In the next paper, by [PERSON] and [PERSON] (contribution 6), the assessment of rock elasticity modulus is conducted using four hybrid random forest models. The study proposes a data-driven method based on the hybrid random forest for the determination of rock elasticity modulus, which is vital for rock engineering design and cannot be solved using traditional methods such as experimental analysis and empirical formulas. In this new method, four metaheuristic optimization algorithms (backtracking search optimization algorithm, multi-verse optimizer, golden eagle optimizer, and poor and rich optimization algorithm) are used to optimize the random forest, thus enabling the construction of four hybrid random forest models. Based on the collected database consisting of 120 rock samples, the hybrid random forest models are applied to predict the rock elasticity modulus according to four factors (porosity, P-wave velocity, Schmidt hammer rebound number, and point load index). Moreover, the performance of the four hybrid random forest models is evaluated according to four indices (root-mean-square error, mean absolute error, determination coefficient, and Willmott's index). The results show that the prediction accuracy of the hybrid random forest model based on the poor and rich optimization algorithm is the best, and that porosity is the most important factor. The study by [PERSON] et al. (contribution 7) is also focused on estimating the undrained shear strength of clay. In this study, based on the collected database of 202 Finnish clay samples, a CatBoost-Bayesian hybrid model adaptively coupled with modified theoretical equations is constructed to determine the undrained shear strength of clay according to 11 main parameters (organic content, clay content, void ratio, natural water content, liquid limit, plastic limit, effective overburden pressure, preconsolidation pressure, overconsolidation ratio, compression index, and sensitivity). The CatBoost-Bayesian hybrid model, in which the Bayesian optimization algorithm is used to optimize the CatBoost algorithm, is employed to obtain the feature importance level of the 11 parameters and is adaptively coupled with the theoretical equation of undrained shear strength derived from the modified Cambridge model. The constructed model is verified using the experimental samples of Finnish clay. The results indicate that the established model can successfully estimate the undrained shear strength of isotropically consolidated clays. The next study, by [PERSON] et al. (contribution 8), uses machine learning for the prediction of ground subsidence risk in urban areas in Korea.