# Instability Risk Assessment for Deep Excavation of Soil-Rock Combinations Containing Groundwater
## 1 Introduction
In addition, the failure mechanics of surrounding rocks affected by influencing factors such as anisotropy and heterogeneity have been investigated by researchers [21; 22; 23; 24]  
With the emergence of artificial intelligence, artificial intelligence learning algorithms based on neural networks have gradually become one of the key methods for geotechnical engineering risk prediction and early warning [25; 26; 27; 28]. [PERSON] et al. [29] proposed a combined prediction model based on an optimized gray discrete Verhulst model and BP neural network and proved its high prediction accuracy and stability by applying the model to predict the settlement of actual building pits. [PERSON] et al. [30] established a comprehensive evaluation index system for the risk of a subway station by analyzing the influencing factors on excavation construction, on the basis of which a three-stage fuzzy comprehensive evaluation model was proposed and the risk level was ranked. [PERSON] et al. [31] proposed a risk assessment model for excavation engineering based on mixed fuzzy set theory, which was successfully applied in actual projects. [PERSON] and [PERSON] [32] proposed a method based on an artificial neural network for predicting the surface settlement of deep excavation, which was used to evaluate the risk of construction in combination with monitoring data. [PERSON] et al. [33] proposed a deep learning model based on a bidirectional long short-term memory neural network combined with self-attention mechanism for predicting the vertical displacement of buildings adjacent to excavation, which was validated using an actual subway project. [PERSON] et al. [34] proposed an analytical method based on artificial neural networks and a uniform design for predicting the probability of damage in deep rock excavations and verified its validity by comparing the results with those of Monte Carlo simulation and a polynomial response surface methodology. [PERSON] et al. [35] analyzed the relationship between different factor variables and the degree of excavation safety with a sensitivity analysis using artificial neural networks. [PERSON] et al. [36] established a real-time online prediction model for surface settlement during foundation excavation based on a long short-term memory neural network with improved quality of input data using grey relational analysis and demonstrated its advantages by applying it to a specific project. [PERSON] et al. [37] proposed a ground settlement prediction model for predicting the potential risk to the surrounding buildings caused by excavation, which used a BP neural network to predict the settlement, while the validity of the model was verified by comparing the prediction results with the actual data. [PERSON] et al. [38], in order to improve the accuracy of excavation engineering prediction, considering the uncertainty in the nature of geotechnical material, proposed an excavation response model, which learns the relationship between geotechnical material parameters and excavation response through a bidirectional long short-term memory neural network and can realize the rapid prediction of excavation response. Currently, neural network-based prediction models have been widely used in excavation projects, with excellent results [39; 40; 41; 42]. Overall, the development and application of risk assessment has helped to reduce the number of engineering accidents. In the abovementioned research, more attention was paid to a certain project or a certain geological condition with respect to the selection of risk evaluation indexes. Prediction using the existing assessment models is not satisfactory in the Qingdao area due to its unique geological characteristics: a typical upper-soft and lower-hard composite stratum. On the other hand, the choice of risk indexes focuses more on explicit factors such as geological conditions and construction design, making it difficult to dynamically assess the risk of excavation based on these models.