## 1 Introduction
the stability of hanging wall rocks, offering a reliable experimental approach that closely mirrors actual dilution phenomena observed during stope development. This technique has proven particularly effective in scenarios involving steeply dipping ore bodies [30; 31]. The equivalent linear overbreak slough (\(ELOS\)) concept, introduced by [PERSON] and [PERSON] [28] and [PERSON] [6], quantifies overbreak/slough volumetrically by converting true volumetric measurements into an average depth over the entire stope surface, calculated as follows using Equation (1):  
\[ELOS=\frac{Volume\ of\ slough\ from\ stope\ surface\big{(}\text{m}^{3}\big{)}}{ Stope\ height(\text{m})\times Wall\ strike\ lenght(\text{m})} \tag{1}\]  
This approach is attractive due to its simplicity and ease of interpretation compared to the use of volumetric measurements. For instance, a 10 m wide stope with an \(ELOS\) of 1 m corresponds to approximately 10% unplanned dilution. \(ELOS\) values can be directly measured from post-blast cavity dimensions. [PERSON] and [PERSON] [28] developed a classification scheme to define stability zones based on \(ELOS\) values [28][PERSON]  
Given that dilution results from multiple independent factors, including overbreak and underbreak, statistical, optimization, and artificial [PERSON] (AI)-based approaches have been developed for its prediction. [PERSON] and [PERSON] [32] used linear regression to assess relationships between factors such as the powder factor, stope geometry, modified rock mass quality index (Q\({}^{\prime}\)), and stope overbreak in a Canadian underground mine. [PERSON] and [PERSON] [28] and [PERSON] [6] proposed four logistic regression models and four artificial neural network (ANN) models to predict \(ELOS\) in six open stoping operations in Canada, also plotting Mathews graphs from these results. [PERSON] [3] used multi-linear regression analysis (MLRA) to predict \(ELOS\) based on stress, undercutting, blasting, and stope exposure time in three Canadian underground mines. The study found that exposure time and hydraulic radius (HR) were the most influential factors, followed by the modified stability number (N\({}^{\prime}\)) and the stope hanging wall undercutting factor (UF). [PERSON] and [PERSON] [33] compared the results of four ANN configurations with statistical approaches such as [PERSON] and multi nonlinear regression analysis (MNRA) models to predict overbreak from underground mines using six independent parameters. [PERSON] et al. [34] developed a wavelet neural network (WNN), a feedforward network with a wavelet transfer function, to predict overbreak in tunnel excavations in China, considering parameters related to the structural plane orientation, trace length, and spacing. [PERSON] [35] and [PERSON] et al. [36] proposed MLRA, MNRA, and a hybrid conjugate gradient algorithm (CGA) with ANN models to predict unplanned dilution and ore loss from three underground long-hole and open stoping mines in Australia, containing information on ten independent variables. The authors used MLRA coefficients to exclude less influential variables, concluding that the CGA-ANN approach outperformed the others. [PERSON] [35] also applied the connection \begin{table}
\begin{tabular}{c c} \hline
**ELOS Range** & **ELOS Design Zones** \\ \hline \(ELOS<0.5\) m & Blast damage only; surface remains \\  & self-supporting. \\  & Minor sloughing; some failure of the \\  & unsupported stope wall must be \\  & anticipated before achieving stability. \\  & Moderate sloughing; significant wall \\  & failure expected before achieving stability. \\  & Severe sloughing; large wall failures \\  & expected, with potential for collapse. \\ \hline \end{tabular}
\end{table} 
Table 1: Classification of stability zones based on \(ELOS\) values [28].