## 1 Introduction
\begin{tabular}{l c c c} \hline \hline
**Empirical Formula** & **Number of Datasets, \(n\)** & **R\({}^{2}\)** & **Reference** \\ \hline \(BAV=-4.64 CAI+25.06\) & 15 & 0.83 & [9] \\ \hline \(BAV=-1.2363 SHV+94.648\) & 6 & 0.66 & [19] \\ \hline \(BAV=579.97V_{p}-2.4279\) & & & 0.85 & \\ \hline \(BAV=69.578 PLS^{-1.4807}\) & & & 0.76 & \\ \hline \(BAV=8.935\exp(0.0857n_{e})\) & 19 & & 0.89 & [20] \\ \hline \(BAV=10553 SHV-1.6868\) & & & 0.92 & \\ \hline \(BAV=50.685\exp(-0.2134 PLS)\) & & & 0.85 & \\ \hline \(BAV=112.87\exp(-0.043 SHV)\) & 14 & & 0.75 & [21] \\ \hline \(BAV=-7.8496\gamma_{d}+223.5\) & & & 0.81 & \\ \hline \(BAV=4.8095n_{e}+12.046\) & & & 0.83 & \\ \hline \(BAV=143.14\exp(-0.039 SHV)\) & 32 & & 0.70 & [22] \\ \hline \(BAV=-37.17\ln(UCS)+193.7\) & & & 0.70 & \\ \hline \(BAV=-37.62\ln(\gamma_{d})+123.21\) & & & 0.90 & \\ \hline \(BAV=1.0408w_{a}+0.5077\) & 22 & & 0.94 & [23] \\ \hline \(BAV=1378.4 UCS^{-1.76}\) & & & 0.65 & \\ \hline \(BAV=-210.3\ln(\rho_{d})+229.92\) & & & 0.81 & \\ \hline \(BAV=1170 UCS^{-1.008}\) & 6 & & 0.76 & [24] \\ \hline \(BAV=-19.167w_{a}+17.62\) & & & 0.75 & \\ \hline \(BAV=2.697w_{a}+14.901\) & & & 0.74 & \\ \hline \(BAV=1.559n_{e}+14.253\) & & & 0.73 & \\ \hline \(BAV=0.024 SHV^{2}-3.255 HV+128\) & 119 & & 0.72 & [25] \\ \hline \(BAV=280.46\times SHV^{-0.435}\times w_{a}{}^{0.1}\times PLS^{-0.572}\) & & & 0.88 & \\ \hline \(BAV=173.2 CID^{2}-61.0 CID+10.6\) & 50 & 0.89 & [26] \\ \hline \(BAV=55.2 CAI^{-1.16}\) & 80 & 0.67 & [27] \\ \hline \end{tabular} \begin{tabular}{l c c} \hline \hline
**Empirical Formula** & **Number of Datasets, \(n\)** & **R\({}^{2}\)** & **Reference** \\ \hline \(BAV=-4.64 CAI+25.06\) & 15 & 0.83 & [9] \\ \hline \(BAV=-1.2363 SHV+94.648\) & 6 & 0.66 & [19] \\ \hline \(BAV=579.97V_{p}-2.4279\) & & & 0.85 & \\ \hline \(BAV=69.578 PLS^{-1.4807}\) & & & 0.76 & \\ \hline \(BAV=8.935\exp(0.0857n_{e})\) & 19 & & 0.89 & \\ \hline \(BAV=10553 SHV^{-1.6868}\) & & & 0.92 & \\ \hline \(BAV=50.685\exp(-0.2134 PLS)\) & & & 0.85 & \\ \hline \(BAV=112.87\exp(-0.0435 HV)\) & 14 & & 0.75 & [21] \\ \hline \(BAV=-7.8496\gamma_{d}+223.5\) & & & 0.81 & \\ \hline \(BAV=4.8095n_{e}+12.046\) & & & 0.83 & \\ \hline \(BAV=143.14\exp(-0.039 SHV)\) & 32 & & 0.70 & \\ \hline \(BAV=-37.17\ln(UCS)+193.7\) & & & 0.70 & \\ \hline \(BAV=-37.62\ln(\gamma_{d})+123.21\) & & & 0.90 & \\ \hline \(BAV=1.0408w_{a}+0.5077\) & 22 & & 0.94 & [23] \\ \hline \(BAV=1378.4 UCS^{-1.76}\) & & & 0.65 & \\ \hline \(BAV=-210.3\ln(\rho_{d})+229.92\) & & & 0.81 & \\ \hline \(BAV=1170 UCS^{-1.008}\) & 6 & & 0.76 & [24] \\ \hline \(BAV=-19.167w_{a}+17.62\) & & & 0.75 & \\ \hline \(BAV=2.697w_{a}+14.901\) & & & 0.74 & \\ \hline \(BAV=1.559n_{e}+14.253\) & & & 0.73 & \\ \hline \(BAV=0.024 SHV^{2}-3.255 HV+128\) & 119 & & 0.72 & [25] \\ \hline \(BAV=280.46\times SHV^{-0.435}\times w_{a}{}^{0.1}\times PLS^{-0.572}\) & & & 0.88 & \\ \hline \(BAV=173.2 CID^{2}-61.0 CID+10.6\) & 50 & 0.89 & [26] \\ \hline \(BAV=55.2 CAI^{-1.16}\) & 80 & 0.67 & [27] \\ \hline \end{tabular} 
\begin{tabular}{l c} \hline \hline
**Empirical Formula** & **Number of Datasets, \(n\)** & **R\({}^{2}\)** & **Reference** \\ \hline \(BAV=-4.64 CAI+25.06\) & 15 & 0.83 & [9] \\ \hline \(BAV=-1.2363 SHV+94.648\) & 6 & 0.66 & [19] \\ \hline \(BAV=579.97V_{p}-2.4279\) & & & 0.85 & \\ \hline \(BAV=69.578 PLS^{-1.4807}\) & & & 0.76 & \\ \hline \(BAV=8.935\exp(0.0857n_{e})\) & 19 & & 0.89 & \\ \hline \(BAV=10553 SHV^{-1.6868}\) & & & 0.92 & \\ \hline \(BAV=50.685\exp(-0.2134 PLS)\) & & & 0.85 & \\ \hline \(BAV=112.87\exp(-0.0435 HV)\) & 14 & & 0.75 & [21] \\ \hline \(BAV=-7.8496\gamma_{d}+223.5\) & & & 0.81 & \\ \hline(TS), uniaxial compressive strength (UCS), and deformation modulus (Et) were used as input parameters. Similarly, [PERSON] and [PERSON] [28] used several artificial neural network (ANN) algorithms to develop comprehensive predictive models for BAV.