# Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
## 3 Materials and Methods
### Case Study and Data Preparation  
A tunnel project (PSRWT, Pahang-Selangor raw water transfer) with a total length of 44.6 km and a diameter of 5.23 m was planned and constructed in Malaysia. This tunnel aims to transfer water from Pahang state to Selangor state. PSRWT tunnel was excavated in a mountain area of Peninsula Malaysia, where six major faults were observed with an elevation range from 100 to 1200 m. In the field, rock strength is poor in areas of fault intersections and highly to moderately weathered zones were observed along the tunnel. Different rock types including shale, coarse-grained granite, and medium-grained granite were observed in various tunnel distances (TDs) of PSRWT tunnel. Regarding construction techniques for the tunnel excavation, there were seven parts, comprising four parts of NATM and three parts of TBM. In PSRWT tunnel project, 11,761 m in the mixed ground, 11,761 m in very hard ground and 11,218 m in the blocky ground were excavated by three TBMs. Construction's of PSRWT tunnel with their maximum overburden are shown in Figure 1.  
There is a need to have a suitable database with the most effective parameters of TBM performance for prediction of TBM PR. The most effective parameters on TBM performance can be identified by reviewing the previous related studies. Based on the literature, three groups of parameters are found as the most effective factors of TBM performance, including machine characteristics, rock mass, and rock material properties [84]. According to [PERSON] and [PERSON] [36], the most important parameters affecting the TBM performance are rock quality designation (RQD), UCS, rock-mass weathering, and rock mass rating (RMR). As some researchers (e.g., [52; 53]) have concluded and pointed out, the rock mass weathering can significantly affect the TBM PR. [PERSON] et al. [85] found that UCS and RMR can significantly affect the TBM performance. They also noticed that the Brazilian tensile strength (BTS) could be set as an input parameter. [PERSON] et al. [19] found an inverse relationship between PR and UCS. [PERSON] et al. [20] showed in their research that factors such as RQD, the diameter of tunnel, UCS, type of rock, disc-cutter normal force, and revolution per minute (RPM) were of the highest influence on PR. Whereas, [PERSON] et al. [45] introduced a different list in this term, including UCS, BTS, intact rock brittleness (BI), cutter head power (CP), cutter-head torque (CT), the distance between the plane of weakness (DPW), and thrust force (TF). An investigation of about 13 km was conducted in the PSRWT tunnel and a database with 209 datasets was prepared. Rock type in the investigated areas was granite covering three weathering zones, i.e., fresh, slightly weathered, and moderately weathered. According to International Society of Rock Mechanics (ISRM) [86], a typical rock weathering profile is composed of six weathering grades namely fresh, slightly weathered, moderately weathered, highly weathered, completely weathered, and residual soil. This classification is mainly based on discoloration and decomposition of the rock material. Based on tunnel mapping 34,740 m of PSRWT tunnel which was excavated by TBMs, a total of 12,649 m comprising 5443 m in fresh, 5530 m in slightly weathered, and 1676 m in moderately weathered zones, was observed. In this study, in terms of rock mass properties, many points in three TBMs were investigated and several rock mass properties, such as type of rock, strength, degree of weathering, joint condition (e.g., spacing of discontinuities, alteration, degree of roughness, infilling material), and groundwater condition, were quantified and database established. Furthermore, machine characteristics, such as TF, RPM, PR, AR, boring energy, stroke speed, and cutterhead torque, were recorded by TBMs.