## 4 Results and Discussion
### Concluding Remarks  
Performance evaluations indicate that the established predictive models can be effectively used to estimate the BAV of various dimension stones. Their accuracy increases significantly when the models are applied within the specified intervals of the non-destructive properties (Table 3). On the other hand, non-destructive rock properties (\(\rho_{\text{d}}\), \(\text{w}_{\text{a}}\), and \(\text{V}_{\text{p}}\)) enable one to implement these models more efficiently. In particular, Figure 8 suggests that \(\text{w}_{\text{a}}\) may be a critical factor when assessing BAV. Last but not least, for the sake of clarity and ease of use, a novel software application was developed to incorporate the proposed predictive models.  
This software, called the Bohme Abrasion Value (BAV) Calculator (version 1.0), provides an accessible tool for estimating BAV values. The BAV Calculator was developed  
Figure 8: Variation in BAV values with respect to \(\text{w}_{\text{a}}\). (revised the x-axis explanation).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \multicolumn{3}{c}{**MAPE**} & \multicolumn{1}{c}{\multirow{2}{*}{\(n\)}} & \multicolumn{1}{c}{**BAV Range**} & \multicolumn{1}{c}{**Dataset**} & \multicolumn{1}{c}{**Reference**} \\ \hline
**MARS** & **M5P** & **ANN** & & **(cm\({}^{3}\)/50 cm\({}^{2}\))** & **Skewness** & **Reference** \\ \hline
39.70 & 37.49 & 30.40 & 30 & 6.83–89.32 (25.45) & 2.46 & [22] \\ \hline
15.75 & 30.65 & 14.17 & 17 & 14.55–80.85 (34.94) & 1.34 & [41] \\ \hline
29.59 & 44.64 & 22.23 & 9 & 15.50–92.00 (44.24) & 1.07 & [42] \\ \hline
43.30 & 53.01 & 52.46 & 13 & 5.58–87.02 (31.87) & 1.24 & [43] \\ \hline
33.82 & 46.50 & 34.95 & 22 & 5.21–46.74 (23.45) & 0.31 & [44] \\ \hline
14.138 & 10.77 & 10.46 & 123 & 5.28–60.07 (15.38) & 3.93 & This study \\ \hline MAPE (\%) & \multicolumn{1}{c}{Predictive model performance [45]} & & & & \\ \hline \(\leq\)10 & \multicolumn{1}{c}{Very good} & & & & \\ \hline
10–20 & \multicolumn{1}{c}{Good} & & & & \\ \hline
20–50 & \multicolumn{1}{c}{Acceptable} & & & & \\ \hline \(>\)50 & \multicolumn{1}{c}{Unacceptable} & & & & \\ \hline \end{tabular}
\end{table}
Table 5: Performance evaluation of the predictive models based on different datasets.  
using MATLAB (Design App Toolbox), allowing users to easily obtain BAV values by inputting adopted non-destructive rock properties.  
Some outputs obtained from the software are illustrated in Figure 9, demonstrating its functionality and potential practicality in the dimension stone industry. The BAV Calculator offers a user-friendly interface, providing quick and reliable BAV outcomes based on the established predictive models.  
Thanks to the established predictive models, the present study can provide a significant contribution to the dimension stone industry by facilitating quick and reliable decision-making processes. It is achieved through the utilization of nondestructive rock properties as input parameters, which are not only cost-effective but also highly repeatable in the laboratory. Furthermore, the BAV tests are relatively expensive, time-consuming, and environmentally detrimental because the use of abrasive powders leads to environmental and occupational health concerns. By focusing on nondestructive methods, the present study may reduce the dependency on traditional BAV testing, paving the way for more sustainable and efficient practices in evaluating the abrasion resistance of dimension stones.