Assigning Resistivity Values to Rock Quality Designation indices Using Integrated Unmanned Aerial Vehicle and 2D Electrical Resistivity Tomography in Granitic Rock

Rock quality designation (RQD) is a standard technique in mining and geotechnical investigation for quantifying the quality and degree of jointing of the rock mass. However, the need for expeditious and inexpensive geotechnical site characterization is the main motivation for the modi�cation of RQD. This research work integrates 2D Electrical Resistivity Tomography (2D ERT), Unmanned Aerial Vehicle (UAV) and borehole to assign resistivity values to various RQD indexes. The UAV survey was performed to calculate RQD on the rock surface using volumetric joint counts (Jv), whereas the 2D ERT survey provides the corresponding resistivity values. A limited number of core samples were also gathered to validate the o the 2D ERT investigation. Combine 2D ERT and UAV provides resistivity values for various RQD indices such as very poor rocks (<350 Ω m), poor rock (350-1150 Ω m), fair rock (1150 – 1850 Ω m), good rock (1850 -2500 Ω m) and > 2500 Ω m excellent rock. Based on the established correlation of RQD and resistivity, the subsurface rock mass quality at Site 1 was predominantly good and excellent rock, while at Site 2, poor rock was signi�cantly reported. This research study concluded that rock mass characterization using RQD is more rapid and inexpensive than the direct or indirect calculation of RQD.


Introduction
Rock quality designation (RQD) is a standard technique in mining and geotechnical investigation for quantifying the quality and degree of jointing of rock mass based on the RQD index (Haldar 2013).Drill core is the primary way of estimating RQD (Zheng et al. 2018).Estimating RQD using drill cores provides reliable information at speci c points; however, large numbers of cores are required for accurate and detailed subsurface geological descriptions, which may be excessively costly and time-consuming.To add more, the estimation of RQD from drill cores is orientation biased (Peng et  Additionally, as many rock-cut slopes are located in complex morphological areas, drilling investigation is not always possible in many cases (Alemdag, Sari, and Seren 2022).To circumvent the limitation of core drilling, Priest and Hudson (1976) suggested the indirect estimation of RQD from discontinuity frequency termed a scanline survey.However, the calculation of RQD from the scanline survey is directional dependent.To reduce the limitation of the scanline survey, Palmstrom (1982) presented a correlation between Jv and RQD.The Jv refers to a number of joints present in a unit volume of the rock mass.
Advanced remote sensing techniques have made extracting data regarding discontinuities spacing from 3D point cloud easier and more rapid.Currently, UAV is the most widely applied technique for geotechnical and rock engineering applications because of its ability to provide an accurate, denser, richer, and more precise 3D point cloud (Wingtra 2021

2018
).Nevertheless, UAV is a widely applied technique for numerous rock engineering applications, but it suffers certain limitations.It is generally believed that UAV is a rapid and quick technique, but the estimation of Jv from the UAV 3D point cloud is time-consuming, particularly for large areas.In addition, the calculation of RQD on a 3D point cloud is not possible in a highly vegetated area.Furthermore, the UAV point cloud provides surface assessment only, while subsurface rock mass quality remains unexposed.
To overwhelm the above-mentioned shortcoming, 2D ERT provides a promising approach (Hasan and Shang 2022).2D ERT offers a low-cost, reliable, advanced data collection and interpretation and a nondestructive approach for subsurface rock mass investigation (Bharti et

Geological Study Area
Three rock slopes, namely Site 1 and Site 2, were opted to achieve the desired objectives.These three sites opted because both have granitic formations.

Site 1
Site 1 is located on one of the longest expressways in Malaysia.This expressway links many major cities of Malaysia, acting as the backbone of the peninsula's west coast.The general geology of the study area is underlain by granitic bodies of Main Range Granite ((JMG) 2014).The rock slope is a part of the Gunung Kledang range located in Kinta Valley.The study area is underlain by massive and homogeneous igneous granitic rock, which forms terrain with high reliefs exceeding 700 m (CM 2014).The Kledang Range, which is in the west of Kinta valley about 40 km long.Figure 1a shows the map of peninsular Malaysia, while Fig. 1b indicate the study area.The red lines in Fig. 1b illustrate the resistivity lines carried out at the top and middle of the study area.

Site 2
The rock slope at Site 2 exists along a newly established highway in Johor, Malaysia, as shown in Fig. 2b.This highway is considered quite busy owing to the proximity of an industrial zone and many nearby villages.
Referring to the "Johor Geological Map" by the Mineral and Geoscience Department of Malaysia, the general geology of the study area is underlain by granitic bodies with acidic intrusion having a high content of silica ((JMG) 2014).The granitic rock in the study area is composed of phyllite, shale, slate, and subordinate sandstone and schist.The elevation with respect to mean sea level at the selected Site ranging 15-48 m.Site 2 is shown in Fig. 1c, while the red line in Fig. 1c represents the resistivity line on the top of the rock slope.The slope height at the study site ranges from 10 m to 15 m.

Site 3
The rock type in the study area is believed to be Triassic Main Range granite (Hutchison and Tan 2009).The ground elevation of the area is in the range of 150-250 m. Figure 1d represent the extent and boundary of the study area.

Methodology
This section provides details about the methodology applied to achieve the targeted objectives.Three types of surveys, such as UAV and 2D ERT and Borehole surveys, were performed, with the detail provided in the following section.At Site 1 and Site 2, integrated UAV and 2D ERT was performed, while at Site 3 borehole and 2D ERT survey was conducted.

UAV Survey Data Acquisition
A quadcopter UAV system using DJI Phantom 4 v2.0 (Fig. 2a) having an onboard 20 MP camera was deployed for UAV survey acquisition at Site 1 and Site 2. The system is wirelessly connected to the DJI iPad controller.The maximum ight time is approximately 30 minutes, with a maximum speed of 20 m/s.The vertical and horizontal hover accuracy of the system with GPS positioning is 0.5 m and 1.5 m, respectively (dji).
The UAV eldwork consisted of three main steps: 1) Flight planning, 2) Ground control points (GCP) acquisition, and 3) ight operation and capturing images.The ight plan of the UAV to capture images for Site 1 and Site 2 is provided in Fig. 3a and 3b, correspondingly.The images at Site 1 were captured at the height of 180-260 m with respect to Mean Sea Level (MSL), whereas, at Site 2, images were taken from approximately 5-30 m height approximately.The positions of the GCPs at both sites were established using Real-Time Kinematic Global Navigation Satellite System (RTK GNSS).For this purpose, the SOUTH G1 GNNS system was utilized, as depicted in Fig. 2b.A total of 6 GCP points at Site 1 and 10 at Site 2 were acquired for georeferencing and accuracy assessment of the 3D UAV point cloud.The aerial images captured at Site 1 and Site 2 were 245 and 246 images, respectively.An overlap of 70% between adjacent images was maintained for capturing the aerial images.

UAV Data Processing
The 3D point cloud was reconstructed from these captured images using ShapeMetriX (SMX) UAV software, following the steps discussed below (3GSM).
(1).The captured aerial images were rst loaded into the SMX multi-photo to reconstruct the 3D coarse point cloud using the structure from motion (sfm) technique.After loading all the captured images, the 3D coarse point cloud is reconstructed.The time needed by the software to construct a 3D coarse point cloud depends on the number of photos captured.The 3D coarse point cloud reconstruction determines the orientation and position of the camera among all photographs relative to each other and relative to the coarsely reconstructed object.
(2).Once the 3D coarse point cloud is reconstructed, the region of interest (ROI) has opted manually.The purpose of choosing ROI is to remove the unwanted points to save processing time.
(4) In the next step, the 3D dense point cloud is constructed.The 3D dense point cloud allows for to calculation of the detailed object geometry, including a detailed point cloud, surface, and mesh texture.
(5).Finally, the georeferencing of the 3D point cloud is carried out to assess the accuracy.In this research work, the georeferencing is carried out in Universal Transverse Mercator (UTM) coordinate system.The obtained 3D point cloud is further processed to calculate RQD, as discussed below.

2D ERT Survey
The 2D ERT survey of both sites was accomplished using Schlumberger protocol with 10 m electrode spacing at Site 1 while 3 m spacing at Site 2. The 2D ERT survey of Site 1 consisted of two lines, namely Line 1 and 2, as shown in Fig. 1b.All the resistivity survey lines at Site 1 were laid longitudinally.
Resistivity L1 was accomplished at the top and L2 in the middle of the rock slope.The entire slope of the Bukit Jelapang was covered using roll-along survey technique.The length of resistivity line L1 was 600 m, and L2 was 1000 m.A total number of 61 electrodes at L1 and 101 for L2 (one centre electrode) equally spaced at 10 m in Schlumberger protocol was utilized in this research work.At Site 2, only one resistivity line, L3, was carried out (see Fig. 1c).The length of the survey line was 120 m.A total number of 41 electrodes with 3 m spacing in Schlumberger protocol was utilized.At the meantime, at Site 3, L4 and L5 as shown in Fig. 1d were accomplished, having 400 m in length each.The inner and outer cables electrode spacing at Site 3 for both lines were 5 m and 10 m, respectively.A total number of 61 electrodes were used at Site 3. The detail of the resistivity lines length, direction and electrode spacing for both sites are summarized in Table 1.

2D ERT Data Processing
To obtain the subsurface interpretation, the apparent resistivity data was processed using ZondRes2DIn software (Zond).The generation of 2D resistivity tomography is a two-step process.Firstly, the geomodelling of the data using forward modelling followed by inversion (least square optimization technique) was carried out.In geomodelling, the subsurface is divided into many cells, and the apparent resistivity value for each block is determined.Then, the iteration of the data iteration was carried out until the optimum root mean square (RMS) error was achieved.An RMS error of 5.15%, 10.1%, and 3.49% was recorded after 13, 13, and 10 iterations for resistivity lines L1, L2, and L3.In comparison, the RMS error of 7 and 5.5% was obtained after 14 and 8 iterations for L4 and L5.

Calculation of RQD from UAV Point Cloud
At Sites 1 and 2, vertical sections equal to slope height were obtained at 15 m intervals, as shown in Fig. 4. Consequently, at Site 1, 41 vertical sections, while seven sections at Site 2 were obtained.These vertical sections were further divided into 1 m*1 at blocks to estimate Jv.Each 1 m* 1 m block provides the value of Jv that was used to calculate the RQD utilizing Eq. (1).Eq. 1 in this research was used as the RQD was calculated from at blocks, and it is more appropriate for at blocks (Zheng et al. 2020).Site 1 and Site 2 were divided into 182 and 41 blocks, respectively.In contrast, two boreholes, BH1 and BH2, as manifested in Fig. 1d were drilled at Site 3 to estimate the RQD value using a direct way of calculating RQD (Deere 1964).The diameter of the recovered borehole sample of BH1 and BH2 was 54 mm, and the depth was 21 m and 22 m, respectively.

Obtaining Resistivity Values for Various RQD Indexes
Similarly, as discussed above, the 2D ERT survey line was split into vertical sections with equal slope heights.These vertical sections were further divided into 1 m*1 m at blocks.Consequently, 182 blocks were obtained at Site 1 and 41 at Site 2. The vertical sections and 1 m* 1 m block were obtained from the 2D ERT pro le.Each block represents a resistivity value for corresponding RQD values.In contrast, the resistivity values from Site 3 were extracted in Golden Surfer software at the same place where the borehole was drilled.

k-Nearest Neighbor (k-NN)
The primary aim of k-NN is to categorize the data into various classes.In this study, the k-NN algorithm in python was used to assign the resistivity values to various RQD indexes.In the k-NN model, 224 data points were used to represent RQD and resistivity values for various RQD indexes.
The k-NN classi cation was performed for two types of correlation such as RQD and resistivity, and Jv and resistivity.First, all the required libraries for k-NN classi cation were called in anaconda.Next, the 224 data points representing RQD, resistivity and Jv were loaded.The next step is to choose the optimum value of "k".In this study, after various trial and errors a 'k" value of 3 has opted.At "k" value of 3 the k-NN Modelling in this study have higher accuracy and good performance.After nalizing the "k" value the k-NN model was run between RQD and resistivity and Jv and resistivity independently.

UAV Survey
The obtained 3D point cloud of Site 1 and Site 2 is depicted in Fig. 5a and 5b.The dotes numbered at the toe of the slopes in Fig. 5a, and 5b represent the GCP points.At Site 1, 6 GCPs, while at Site 2, 10 GCPs were used for georeferencing.The accuracy of the GCPs for Site 1 is provided in Table 2, whereas for Site 2, in Table 3.

2D ERT Survey
The resistivity line L1 was located at the top of Site 1, allowing to record 906 data points dividing the subsurface into 787 blocks.Figure 6a shows that resistivity values of 200 Ωm to more than 13000 Ωm were obtained at L1, exposing two subsurface geological layers.These geological layers include a fresh rock with more than 2000 Ωm and a fractured rock with resistivity 300 to 2000 Ωm.The fresh rock is represented by dark reddish colour encircled by black dotted lines, while greenish to yellow colour refers to fractured rock.A fresh rock layer of 15-20 m thickness was reported by L1 at the uppermost region.
Towards the South-west major portion of the subsurface rock mass is the fresh rock.In contrast, the middle part of the rock mass at L1 is composed of fractured rock extending in the Northeast direction.The interpretation of resistivity line L1 shows that a high portion of the subsurface rock mass was moderately fractured rock characterized by yellow colour having resistivity ranges 1000 Ωm to 2000 Ωm.
The resistivity line L2 provided subsurface information using 1654 data points divided into 4000 blocks carried out in the middle of Site 1.As delineated in Fig. 6b, the high portion at resistivity line L2 was found in fresh rock with a resistivity value greater than 2000 Ωm shown by the dark red colour.Towards the North-East and southwest direction, a 15 to 20 m thick layer of moderately fractured rock was identi ed as represented by yellow colour encircled by black dotted lines having resistivity ranges 1000-2000 Ωm.
At the middle of the line near the surface, a water-saturated zone of resistivity values 200-300 Ωm was discerned.The same layer was also exposed towards the northeast, represented by green.Interestingly, the water-saturated zone was associated with fractured rock mass.This authenticates the existence of a water-saturated zone at L2 as the run-off water was in ltered through the fractured rock mass.The dipping of water from the rock surface at Site 1 observed during eldwork is also provided in Fig. 6b.This con rms that this low resistivity layer is a water-saturated zone as water dipping was observed on the slope surface in the same region.
The 2D ERT tomography for all resistivity lines, i.e., L3 at Site 2, is shown in Fig. 7.The subsurface resistivity values by L3 were obtained for 524 data points.The depth of investigation of L3 was around 25 m.The resistivity line L3 discerns three distinct geological layers at Site 2: clay, fractured, and fresh rock.The resistivity value of these geological layers that consisted of clay, fractured rock, and fresh rock ranges of 100 Ωm -200 Ωm, 300 Ωm -200 Ωm, and greater than 2000 Ωm, respectively.A low resistivity layer (< 150 Ωm) around 1 m thickness was observed at the topmost portion.This layer corresponds to the topsoil layer composed of loose material as a result of the excavation process.Immediate to the topsoil layer, a 1 m thick geological layer characterized by yellow colour was identi ed by L3.The resistivity of this layer ranges between 800-1500 Ωm, thus categorized as a fractured rock.At a depth of around 2 m, a thick layer of 4-5 m represented by dark brown colour having a resistivity greater than 2800 Ωm was also discerned by L3.This layer is characterized as a fresh rock.A highly fractured rock manifested by green colour at a depth of 6-7 m having resistivity 250-500 Ωm was also identi ed by L3.Pockets of low resistivity layers (< 250 Ωm) were also noticed at a depth around 9 to 10 m, represented by blue colour by L3.This layer was termed because the clay was observed during a eld visit in the rock joints at the slope's lower portion.The extremely fractured rock mass along with clay in lling on the slope surface is also shown in Fig. 7b and c.
Resistivity line L4 is depicted in Fig. 8a.In the topmost region up to 15 m thick, a low resistivity layer of less than 250 Ωm was identi ed as represented by light blue colour.This layer was termed the topsoil layer as no core was recovered by BH-1 in this region.The subsurface rock at the centre and towards the west is more fractured, represented by green and yellow colours.However, a higher portion of the subsurface rock at L4 is solid rock, as shown by its reddish colour.On the other hand, resistivity pro le L5, as manifested in Fig. 8b, exposed the three types of subsurface geological layers.The uppermost layer up to 15 m in thickness, shown by light blue colour, was reported as a topsoil layer having resistivity less than 200 Ωm.A fracture.The solid layer having a resistivity greater than 2000 Ωm is recognized by its reddish colour.A small portion of fractured rock with resistivity ranges 300 to 1800 Ωm is identi ed by greenish and yellow colour at L5.

Obtained RQD and Resistivity Values
Cumulatively 224 data points were obtained from Site 1 and Site 2, representing RQD and corresponding resistivity values.Out of 224 data points, 183 data points were contributed by Site 1 and 41 by Site 2. The data points for very poor, fair, and good rock at Site 1 were 7, 78, 66, and 32 provided in Fig. 9a.At Site 2, out of 41 data points, 18, 13, and 10 data points were obtained for poor, fair, and good rock, respectively shown in Fig. 9b. Figure 9 reveals that the data points obtained in the present research comprise the RQD and corresponding resistivity values for almost all RQD indexes.This con rms that these data points can be utilized to establish a correlation between RQD and resistivity value.
At Site 3, a total of 4 data points were obtained representing RQD and corresponding resistivity values from core samples and a 2D ERT survey.The detail thickness and RQD values obtained for these core samples are presented in Table 4.The main purpose of coring is to validate the RQD and corresponding resistivity values obtained indirectly from UAV and 2D ERT surveys.

k-NN Modelling
The resistivity values were assigned to various RQD indexes; by opting for k value 3 in k-NN Modelling in this study.Figure 10 shows the predictor space chart of the k-NN model.In this study, the predictor space chart was developed using three input parameters, namely RQD, Jv, and resistivity.The performance of the k-NN model is checked by various parameters such as precision, recall value, F1 score, and support.The obtained values for these parameters are provided in Table 5.The precision value shows the accuracy and reliability of the model.Table 4, the precision for poor rock (0.93) is higher than for good (0.86) and fair rock.(0.90).However, higher accuracy above 0.85 for all rock types shows that the k-NN model exploited in this research work has high precision and reliability.The recall value in k-NN Modelling manifests the capability of the obtained model to identify the relevant data of the group.In this study, the recall value for the fair rock was the lowest, i.e., 0.76.While the recall value obtained for poor and good rock is 0.97 and 0.93, respectively.Overall, the obtained recall value for all rock types is encouraging and thus prove the k-NN model is reliable.
F1-score is the harmonic mean of precision and recall.The F1-score is equally important in k-NN Modelling as precision and recall.The good F1 score re ects that the model has good precision and recall and vice versa.The obtained F1 score from k-NN Modelling for poor, fair and good rock is 0.95, 0.81, and 0.90 in this research work.The high F1 score value proves that the k-NN Modelling in this research study is reliable.However, the RQD classi cation system shows that the RQD for very poor rock is less than 25% and for excellent rock is greater than 90%.In the same scenario concluding Fig. 11, the resistivity of less than 350 Ωm and greater than 2500 Ωm were assigned for very poor and excellent rock, respectively.The ranges of Jv, resistivity and RQD obtained using k-NN Modelling in this study is tabulated in Table 6.This study effectively provides a more extensive correlation of RQD and resistivity values.Furthermore, all the previous studies deployed coring in integration with a 2D ERT survey to calculate RQD and corresponding resistivity values.In comparison, this study adopted an inexpensive and faster approach using integrated UAV and 2D ERT with a limited number of coring.Although, a 2D ERT survey independently may mislead and provide ambiguous results.However, the outcome of this research work is validated with the core data and, therefore, can be applied for various geotechnical site investigations.The correlation of RQD and corresponding resistivity values obtained in this study will enable to characterize of subsurface rock mass quality using a 2D ERT survey based on RQD indices as provided in Fig. 13.Based on the established correlation as presented in Table 6; the subsurface rock mass was classi ed into various classes of RQD.The 2D ERT images were exported to Golden surfer 13 software.A unique and uniform colour was assigned to each RQD class, as shown in Fig. 13.According to Fig. 13, the very poor, poor, fair, good, and excellent rock is represented by light grey, cyan blue, orange, red, and dark red colour.However, at Site 1, the good and excellent rock mass exists dominantly, as represented by Fig. 13(a) and (b).On the other hand, Site 2 is dominantly composed of poor rock, as illustrated by Fig. 13(c).
Figure 13 shows a thorough insight of rock mass quality at both sites compared to ERT interpretations presented in Figs. 6 and 7.The 2D ERT interpretation classi es the subsurface rock mass as fresh and fractured rock.Whereas the correlation between RQD and resistivity established in this study categorized the rock mass into ve classes based on RQD indices.This shows that the outcome of current research studies provides a more extensive subsurface rock mass quality investigation along the rock slope.As mentioned in Fig. 13, the lateral and vertical extent of various subsurface rock mass quality is e ciently identi ed.This information can be relied on to take the necessary actions for the mitigation of rock falls due to slope instabilities.Furthermore, the rock slopes in this study exist over a large area, hence requiring large numbers of boreholes to obtain a detailed and comprehensive subsurface geological image.
However, the capability of 2D ERT to provide a large number of data points enable the comprehensive assessment of subsurface rock mass quality with a limited number of boreholes.Thus, this research work provides an expeditious, effective, inexpensive, and environmentally non-destructive approach for subsurface rock mass characterization based on RQD using 2D ERT.
The resistivity of granitic rocks may have different resistivity in various regions.However, the resistivity values assigned to various RQD indices are in a wider range in this research work.Therefore, it is believed that the resistivity of granitic rock from anywhere will exist in these certain ranges.Therefore, the correlation of RQD and resistivity obtained in this study has applicability to all granitic rock types.Hence, the approach of this study is applicable in most cases.

Conclusion
This study successfully assigns resistivity values to various RQD indices, which was achieved by utilizing k-NN Modelling on data points obtained using 2D ERT and UAV surveys.The conclusion based on this research study is summarized as follows.
2. The resistivity of the rock mass decreases with an increase in the degree of fracturing.
3. The Jv and resistivity were found inversely proportioned.This means that increase in Jv, the resistivity value decreases and vice versa.4. The resistivity and RQD were noticed to be directly proportional.This means an increase in RQD causes increases in resistivity of the rock mass and vice versa.
5. Based on the established correlation of RQD and resistivity, the subsurface rock mass quality at Site 1 was predominantly good and excellent rock, while at Site 2, poor rock was signi cantly reported.
. The calculation of RQD either by direct or indirect technique provides spotted information and may overlook the rock mass quality, while a geophysical survey provides continuous subsurface information.7. The subsurface characterization of rock mass quality using 2D ERT is expeditious, inexpensive, and environmentally non-destructive.
This study deployed an integrated 2D ERT, UAV and boreholes survey to assign resistivity values to various RQD indexes.The UAV survey aimed to reconstruct a 3D point cloud and calculate the RQD values on a rock outcrop indirectly using Jv.The purpose of the 2D ERT survey was to extract the corresponding resistivity values for various RQD indexes.The sole aim of the borehole survey was to validate the RQD and corresponding resistivity values obtained from the UAV and the 2D ERT survey.The k-Nearest Neighbour (k-NN) analysis was performed to assign resistivity values to various RQD indexes.

Figure 11a represents the
Figure11arepresents the k-NN model between RQD and resistivity, while Fig.11(b) is the graphical representation of the correlation of Jv and resistivity.A positive correlation between RQD and resistivity was noticed, whereas Jv and resistivity show a negative correlation.The k-NN Modelling successfully categorized into three distinct RQD classes: poor rock, fair rock, and good rock, represented by red, green, and yellow color marks, respectively Fig.11.The gure re ects that the resistivity of poor rock varies between 350-1150 Ωm, fair rock from 1150 to 1850 Ωm, and good rock from 1850-2500 Ωm.Figure11(a) and (b) shows no correlation of Jv, resistivity, and RQD for very poor rock excellent rock.However, the RQD classi cation system shows that the RQD for very poor rock is less than 25% and for excellent rock is greater than 90%.In the same scenario concluding Fig.11, the resistivity of less than 350 Ωm and greater than 2500 Ωm were assigned for very poor and excellent rock, respectively.The ranges

Figure 11 (
a) and (b) shows no correlation of Jv, resistivity, and RQD for very poor rock excellent rock.

Maps
of the study area a map of peninsular Malaysia b Site 1, c Site 2, d Site 3

Figure 9 Frequency
Figure 9

Figure 11 k
Figure 11

Figure 12 Comparison
Figure 12 al. 2016; Junaid et al. 2021; Falae et al. 2019a; Junaid et al. 2019; Cuong et al. 2016; Alpaslan 2021; Junaid, Abdullah, Saa'ri, et al. 2022).In recent decades, 2D ERT is widely applied for numerous geotechnical assessments with a main aim to balance the investigation accuracy and cost (Liu et al. 2022).The sensitivity of the rock mass resistivity to the fracturing, weathering, and presence of water and clay content in a rock mass also makes it an appropriate technique for slope stability assessment (Boyle et al. 2017; Khan et al. 2017; Asriza et al. 2017; Singh and Sharma 2022).2D ERT is an extensively applied technique for various rock engineering applications such as rock slope stability assessment (Khan et al. 2017; Naudet et al. 2008; Coulibaly, Belem, and Cheng 2017; Falae et al. 2019b), earth dam health assessment (Zumr et al. 2020; Camarero, Moreira, and Pereira 2019), landslide monitoring and slope stability assessment (Urban et al. 2015; Pánek et al. 2011; Carrión-Mero et al. 2021; Buša et al. 2020).However, no attempt has been made to assign resistivity values to various RQD indexes for granite rock.

Table 5
resistivity values obtained from the borehole and 2D ERT survey at Site 3 were compared with those obtained from the UAV and 2D ERT survey at Site 1 and Site 2, as shown in Fig.12.Figure12.These gures clearly re ect that the RQD and corresponding resistivity values obtained from 2D ERT and borehole is in good compromise with that of UAV and borehole.This shows that the 224 data points obtained in this study using integrated 2D ERT and UAV are reliable Accurate characterization of subsurface rock mass quality is necessary to mitigate the economic losses, property damages, injuries, and fatalities due to rock slope instabilities.RQD is a widely applied empirical technique for preliminary assessment as it is the simplest among all rock mass classi cation systems.However, estimation of RQD from traditional borehole gives reliable subsurface imaging but is limited to speci c points only.At the same time, the indirect way of estimating using UAV remote sensing techniques suffers limitations of visualization in vegetated areas.2DERT is found successful in providing continuous and reliable subsurface investigation.However, a comprehensive and detailed correlation of RQD and resistivity for granitic rock does not exist to date.However, few researchers have attempted to correlate the RQD and resistivity of fresh rock(Olona etal.2010; Lin et al. 2021; Ishak et al. 2020; Junaid et al. 2019).Nevertheless, the previous research study correlates resistivity and RQD for the selected values of RQD indices.