## 1 Introduction  
The rapid advancements in computer technology have substantially bolstered both the theoretical foundation and practical applications of numerical analysis in geotechnical engineering. Despite the notable precision of contemporary numerical simulation models, the task of accurately identifying the requisite rock mechanics parameters continues to pose a considerable challenge. This difficulty primarily stems from several factors: the inherent complexity of underground structures, the unpredictability of geological conditions, the significant impact of scale effects in rock masses, and constraints in sample collection [1, 2, 3]. Consequently, accurately ascertaining the necessary rock mechanics parameters for these models has become a paramount challenge, central to the objectives of ongoing research. To address this challenge, the back-analysis methodology was developed to accurately determine rock mechanics parameters, emerging as a trusted technique for pinpointing simulation parameters. Initiated in the 1970s, the technique of dynamic displacement back-analysis has achieved global recognition. Researchers utilize displacement data, monitored throughout the construction process, to inversely deduce the rock mass's physical and mechanical characteristics and its constitutive model, contributing significantly to the field's body of research [4, 5, 6, 7, 8, 9]. [PERSON] et al. [10] proposed the innovative Evolutionary NeuralNetwork, combining artificial neural networks with Genetic Algorithms, demonstrating its effectiveness in back-analyzing elasticity problems. By merging Genetic Programming (GP) with numerical modeling, [PERSON] et al. [11] created a database from simple, measurable parameters through backward analysis, accurately estimating the deformation modulus of rock masses and showcasing the computational program's efficiency and precision. [PERSON] et al. [12] developed a finite element model for slurry shield tunnel excavation, accounting for subsoil parameter variability, and introduced a probabilistic back-analysis method using Monte Carlo simulations to determine subsoil elastic modulus and Poisson's ratio, facilitating surface subsidence predictions resulting from tunneling activities. [PERSON] et al. [13] presented a neural-network-based inversion technique for determining the parameters of surrounding rock, validating its efficiency through case studies from two roadways in the Huianan Coal Mine, China. [PERSON] et al. [14] applied a BP neural network to back-analyze the mechanical parameters of tunnel surrounding rock by utilizing uniform design to create samples, successfully inverting these to determine the dam foundation's mechanical parameters. Owing to the ongoing advancements in machine learning, numerous researchers have utilized artificial intelligence algorithms for the inversion of mechanical parameters of surrounding rock in underground engineering. [PERSON] et al. [15] developed an advanced Support Vector Regression (SVR) model, enhanced by a Multi-Strategy Artificial Fish Swarm Algorithm (MAFSA), to accurately identify the mechanical characteristics of tunnel-encasing rocks. [PERSON] et al. [16] developed the GA-BP neural network model by integrating the improved Genetic Algorithm with Simulated Annealing (GA) to improve the prediction accuracy of the BP neural network for inverting surrounding rock mechanical parameters. [PERSON] et al. [17] proposed a composite inversion model to accurately and efficiently determine the mechanical parameters of tunnel surrounding rock. This model utilizes distinct inversion methods for various mechanical parameters. Research findings reveal that the composite model achieves an average inversion error of 9.32% for rock mechanical parameters, with a minimum error of only 0.81% and errors kept within 15%. Determination of mechanical parameters and their uncertainty remains a major challenge in rock mechanics and engineering.