## 1 Introduction
This analysis identified the angle between discontinuities and tunnel contours, as well as the uniaxial compressive strength, as the most significant contributing factors. [PERSON] et al. [48] proposed an ANN with a single-hidden layer, alongside a hybrid genetic algorithm-ANN (GA-ANN) model, to predict overbreak in tunnel excavation projects in Iran, incorporating seven independent variables, determining that the GA-ANNsignificantly outperformed the standard ANN. A subsequent parametric sensitivity analysis identified the rock mass rating (RMR) as the most influential factor in overbreak prediction. Building on this work, [PERSON] et al. [49] introduced an artificial bee colony-ANN (ABC-ANN) model, leveraging swarm intelligence for optimization and confirming its superior predictive capability when compared to that of standard ANN. Expanding on these findings, [PERSON] et al. [50] enhanced their approach by developing an ANN with a single-hidden layer trained on an extended dataset incorporating nine independent variables. Additionally, to further optimize blasting parameters and minimize overbreak, the authors employed the ABC algorithm, demonstrating its effectiveness in refining the predictive model and enhancing excavation efficiency. [PERSON] and [PERSON] [51] employed an ANN to predict _ELOS_ using a dataset comprising samples from six underground mines in China, considering four independent variables, i.e., modified stability number (N\({}^{\prime}\)), hydraulic radius (HR), average borehole deviation, and powder factor. Additionally, the authors derived a predictive formula based on the trained model, reaching results superior to those obtained by empirical graph and numerical simulation methods. [PERSON] and [PERSON] [52] developed two ANN models to predict unplanned dilution in underground mining operations, considering the values of N\({}^{\prime}\), HR, and actual dilution. In addition to model predictions, the authors generated dilution maps based on classification outputs, providing a visual representation of unplanned dilution distribution within mining stopes. Further extending their work, [PERSON] and [PERSON] [53] proposed an FIS incorporating three membership functions to model the relationship between independent variables (N\({}^{\prime}\) and HR) and unplanned dilution from a mine in Kazakhstan operating under sublevel caving, cut-and-fill stoping, and sublevel stoping mining methods. [PERSON] et al. [54] applied two multicriteria decision-making (MCDM) techniques to assess dilution risk across 10 underground mines in Iran, considering 10 independent parameters. A fuzzy-Delphi analytical hierarchy process was employed to assign relative weights to each parameter; the authors then utilized the multi-attributive approximation area comparison (MABAC) and the technique for order of preference by similarity to ideal solution (TOPSIS) methods to compute a dilution risk score and rank the mines accordingly. The accuracy of these models was validated against actual dilution measurements obtained through a cavity monitoring system, with the MABAC method demonstrating superior precision in classification. [PERSON] et al. [55] leveraged the same dataset to develop an ANN classification model for predicting unplanned dilution, also introducing probabilistic dilution maps generated from ANN predictions, providing a spatial representation of dilution susceptibility within the evaluated mines. [PERSON] [3] investigated unplanned dilution prediction through both statistical and AI-based regression models from a dataset with 68 parameters from an underground mine in North America, employing the longitudinal retreat long-hole mining method. The study evaluated the predictive capabilities of MLRA, MNRA, ANN, RF, and recurrent neural networks (RNN), and the results indicated that the most influential factors in unplanned dilution were the length and height of the unsupported stope, along with the powder factor, while the RF model achieved the best prediction results. [PERSON] et al.