## 2 Methods
### Data Gathering and Case Study  
The authors of this present study gathered data from the surface of the Pahang-Selangor raw water transfer (PSRWT) tunnel in Malaysia. This project was constructed to transport the Selangor's water demand and the state of Kuala Lumpur to Selangor. It is worth noting that the tunnel was passed under the major mountain chain separating Selangor and Pahang states. This mountain chain has an elevation varying from 100 to 1400 m and shaping the backbone of Peninsular Malaysia. Granite is the major rock variety in this tunnel, with a regular rock strength of 150-200 MPa. The excavators unearthed three tunnel parts using three tunnel boring machines (TBMs). The length of these tunnels was 11.7 km, 11.7 km, and 11.3 km.  
For lab tests, the research team prepared 45 rock samples. They also examined the samples for any factor that may lead to an unwanted change in qualities and unanticipated breakdowns. Thus, the samples were inspected for any cracks or tiny-scale discontinuities. Dry density (DD) and Vp were among the rock physical tests. In addition, in order to determine the E, the research team conducted uniaxial compression tests. The rock samples' E were calculated based on the axial strain outcomes and the stress estimated by significant transducers. ISRM [59] standards were implemented during the sample preparation and sample testing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Input Variable** & **Method** & \(\mathbf{R^{2}}\) \\ \hline [PERSON], [PERSON], & & & \\ [PERSON], [PERSON] and & \(I_{s(50)}\), \(n\), \(R_{N}\), \(V_{p}\) & ICA-ANN & 0.71 \\ [PERSON] [1] & & & \\ [PERSON], et al. [54] & \(p\); \(V_{p}\), Qtz, Kpr, Plg, _Chl, Mica_ & ANFIS & 0.99 \\ [PERSON], [PERSON] and [PERSON] [17] & \(p\); \(n\); \(V_{p}\) & GA & 0.67 \\ [PERSON], [PERSON], & & & \\ [PERSON], [PERSON] and & \(R_{n}\), \(V_{p}\), \(I_{s(50)}\) & FIS and ANN & 0.79 and 0.82 \\ [PERSON] [2] & & \(V_{p}\); \(I_{s(50)}\); \(R_{n}\); \(n\) & ANN & 0.77 \\ [PERSON], et al. [55] & \(I_{s(50)}\); BPI, Vp, RTS & FIS & 0.79 \\ [PERSON] and [PERSON] [57] & \(p\); RADD; \(n\); NI, GSI & GA-ANN & 0.89 \\ [PERSON], et al. [58] & \(p\); \(I_{s(50)}\), WA & ANFIS & 0.66 \\ [PERSON] and [PERSON] [15] & \(\mathrm{n_{e}}\), \(I_{s(50)}\), \(R_{n}\); I\({}_{\mathrm{d}}\) & ANN & 0.91 \\ [PERSON] and [PERSON] [14] & \(V_{p}\); \(I_{s(50)}\); \(R_{n}\); WC & ANFIS & 0.95 \\ \hline \hline \end{tabular}
\begin{tabular}{c} BPI = block punch index, GSI = geological strength index, I\({}_{\mathrm{d}}\) = slake durability index, \(n_{\mathrm{e}}\) = effective porosity, NJ = number of joints per meter, RQD = rock quality designation, WA = water absorption, WC = water content, \(n\) = porosity, \(p\) = density, ICA = imperialism competitive algorithm, quartz = Qtz, alkali feldspar = Kpr, plagioclase = Plg, chlorite = Chl, Schmidt hammer rebound number = \(R_{n}\), ultrasonic velocity = \(V_{p}\), point load strength = \(I_{s(50)}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent studies on the use of machine learning techniques to predict the E.  
The data of this research also were used by [PERSON], [PERSON], [PERSON] and [PERSON] [54]. The research team also conducted petrographic analyses on the granite samples, employing a polarizing petrological microscope. In order to determine the portion of various minerals in the samples, small parts of the samples were prepared. The specimens present nonporphyritic and holocrystalline mineral composition and usually chiefly involve alkali feldspar (Kpr), interlocking coarse-grained crystals of quartz (Qtz), biotite (Bi), and plagioclase (Plg). Note that micas are factored by three minerals: sercite, muscovite, and biotite.