## 1 Introduction
weight algorithm and profile method to assess the influence of independent variables, identifying geology, blasting, and slope design factors as contributing 38.79%, 37.17%, and 24.04%, respectively, with Q\({}^{\prime}\) index and the average horizontal-to-vertical stress ratio (K) being the most influential parameters. [PERSON] [35] and [PERSON] et al. [37] also developed a concurrent neuro-fuzzy system (CNFS) to connect ANN with a fuzzy inference system (FIS), where one model assists the other in decision making, with the FIS being optimized by adjusting fuzzy membership functions (FMF) and fuzzy rules, based on expert knowledge. [PERSON] et al. [38] employed linear regression and FIS to predict overbreak in tunnel excavation in Iran from nine independent variables. [PERSON] et al. [39] proposed a classification system to assess and predict unplanned dilution in the cut-and-fill stoping method, leading to the stope unplanned dilution index (SUDI), which quantifies how susceptible a cut-and-fill stope is to unplanned dilution. Their methodology involved identifying 20 parameters, applying a fuzzy Delphi analytical hierarchy process to assign weights, determining the most influential factors and calculating SUDI to categorize slopes into five classes based on robustness against unplanned dilution. [PERSON] et al. [40] employed MLRA, MNRA, ANN, fuzzy logic, adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) to predict overbreak from tunnel excavations and mine sites in Iran and India, encompassing five independent variables. [PERSON] et al. [41] extended this research, comparing ANFIS and particle swarm optimization-ANFIS (PSO-ANFIS) models, then confirming the better performance of the PSO-ANFIS model. [PERSON] et al. [42] applied a random forest (RF) algorithm to predict \(ELOS\) from a dataset with 13 independent variables obtained from a mine in Australia operating under a transverse open slope mining method with delayed backfill, as provided by [PERSON] [43]. The study formulated the problem as a classification task, categorizing \(ELOS\) values greater than one as unstable and those below one as stable. The most influential predictors identified were stope dip, stope strike, stope height, rock quality designation (RQD), and joint set number. In a subsequent study, [PERSON] et al. [44] explored multiple machine learning approaches--including logistic regression, ANNs, decision trees (DTs), gradient boosting machines (GBMs), and SVMs--to predict \(ELOS\) from the same dataset. Additionally, the firefly algorithm (FA) was employed to optimize the hyperparameters of these models, finding that FA-GBM outperformed the other models for the testing set. Building on their previous works and dataset, [PERSON] et al. [45] incorporated techniques such as missing data imputation and semi-supervised learning. The authors optimized the hyperparameters of RF, linear discriminant analysis (LDA), GBM, Gaussian process classification (GPC), SVM, ANN, and logistic regression models using an FA-based metaheuristic. Furthermore, an ensemble model was constructed through a weighted voting approach, thereby surpassing the performance of individual models in terms of classification accuracy. [PERSON] et al. [46] utilized an ANN to predict uneven break in narrow-vein underground mines in Australia, analyzing a dataset comprising 10 independent variables, with a sensitivity analysis indicating that Q\({}^{\prime}\) and K were the most influential parameters. Expanding on this research, [PERSON] et al. [47] introduced the overbreak resistance factor (ORF) to manage overbreak in tunnel drill-and-blast operations from a tunnel excavation in Japan, incorporating six geological parameters as independent variables. Initially, equal weights were assigned to all variables. Subsequently, five ANN models were trained to predict overbreak, followed by a sensitivity analysis using the profile method.