## 2 An Overview of the Published Papers
Because ground subsidence in urban areas, caused by damage to underground utilities, can cause serious disasters, [PERSON] et al. construct a machine learning-based ground subsidence risk prediction model based on the collected attribute information and historical ground subsidence data on six types of underground utility lines (water supply, sewage, power, gas, heating, and communication). Firstly, the target area is divided into a square grid from which the attribute information and historical ground subsidence data are extracted. Twenty-four datasets are developed, including single-type attribute information, merged by six types of underground utility lines, and three risk levels, categorized from the number of ground subsidence occurrences. Then, based on the datasets, three machine learning models (random forest, extreme gradient boosting, and light gradient boosting machine) are applied to classify ground subsidence risk levels. The results show that the new method can successfully predict risk levels in the target region. The study by [PERSON] and [PERSON] (contribution 9) is focused on comparing four machine learning-based soil classification methods using cone penetration test data. In this study, the four machine learning methods include artificial neural network, random forest, support vector machine, and decision trees. The database used is collected from the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) database and includes 232 cone penetration test (CPT) datasets, and soil is classified according to [PERSON]'s soil behavioral types. Moreover, the quantitative metrics used to evaluate the machine learning methods include overall accuracy, sensitivity, precision, F1_score, and confusion matrices. The results show that all machine learning methods can accurately classify most soils, and most evaluation metrics of the four machine learning methods indicate high scores. Moreover, the support vector machine and random forest have outstanding performance on both majority and minority soil classes and can be applied for rapid and accurate soil classification. The article by [PERSON] et al. (contribution 10) is focused on the determination of the damping ratio of clay soils. In this study, to ease the difficulty of determining the properties of soils, which are affected by many complex factors, a new method to determine the value of the clay soil damping ratio using an artificial neural network model is proposed. Based on a dataset consisting of 1227 examples from the testing 15 soil samples in the Resonance Column, the damping ratio of clay soils affected by seven factors (shear strain, normalized shear modulus, liquid limit, mean effective stress, silt content, plasticity limit, and clay content) is predicted. Moreover, a comparison of the new artificial neural network model with empirical formulas is conducted. The results show that the new method boasts a much higher prediction accuracy and a more flexible application. The next study, by [PERSON] et al. (contribution 11), is focused on the prediction of undrained bearing capacity of skirted foundation in spatially variable soils. The bearing capacity of the skirted foundation, widely used in offshore and subsea engineering, is seriously affected by variabilities in soil undrained shear strength. To predict its uniaxial bearing capacity factors under pure horizontal and moment loads, an efficient machine learning method using a two-dimensional convolutional neural network is proposed. In this new method, datasets with 600 samples from the random finite element numerical simulation are used; the input is a random field data matrix for the soil domain in a numerical model, and the output is the corresponding bearing capacity factor. The results show that the prediction performance of the new method is satisfactory in terms of the variation coefficients and the fluctuation scale in two directions, with a high determination coefficient and low root-mean-square error.