DESC:F O R M 2

THE PATENTS ACT, 1970
(39 of 1970)
&
The Patents Rules, 2003
COMPLETE SPECIFICATION
(See section 10; rule 13)

TITLE
A System and a Method for Rock Mass Characterization and Rock Support System in Mining

APPLICANT
MOIL LIMITED
A Government of India Enterprise,
1-A, MOIL Bhawan,
Katol Road, Chaoni,
Nagpur-440 013,
Maharashtra, India

The following specification particularly describes the nature of the invention and the
manner in which it is to be performed.
Field of Innovation: The present invention relates to a method and system for Rock Mass characterization and Rock Support System in Mining for finding Rock Mass Rating classification(RMR)and Baron’s Q value necessary to support the underground excavation taken up for mining.

Background of the Invention:
The potential for instability in the rock surrounding underground mine openings is an ever-present threat to both the safety of men and equipment in the mine. Hence, it is necessary to support the underground excavation.The term ‘support’ is used to cover all types of rockbolts, dowels, cables, mesh, straps, shotcrete and steel sets used to minimize instability in the rock around the mine openings.
For rock reinforcement in underground structures, an engineering analysis begins with evaluation of two fundamental factors;

• The strength of the different components of the rock structures and
• The forces that are loading it.

Underground opening cannot be left permanently unsupported. The decision of ‘support’ versus ‘no-support’ depends on factors such as
• the rock mass properties,
• the span width and
• Type of excavation.
Since unsupported openings range in span from 2 meters up to 100 meters.

The rock mass properties have utmost importance for calculation of rock mass characterization and rock support system. Rock Mass Classification systems used in tunneling & mining is
• i) Terzaghi’s classification (1946)
• ii) Lauffer’s classification (1958)
• iii) Deere’s rock mass classification (1963)
• iv) Wickham’s RSR classification system (1972)
• v) Bieniawski’s Geo-mechanics classification (1973)
• iv) Barton’s rock mass quality, Q (1974)

RMR classification for support of underground structures is introduced during 6th National Safety Conference in 1986 organized by Directorate General of Mines Safety (DGMS) under the Ministry of Labour, India. The RMR classification becomes mandatory for the support of underground structures.
Bieniawski’s Geo-mechanics classification Rock Mass Rating (RMR):
In 1973 Bieniawski brought out a classification of rock mass based on eight parameters. Since each parameter has different contribution to the rock quality, ratings have been assigned to each parameter by a weighted numerical value. In the subsequent classification (1976) six parameters were chosen instead of eight. These are strength of intact rock, RQD, spacing of joints, condition of joints, ground water and the last parameter jointed orientation was dealt separately. The ratings assigned to the different parameters are;

Parameter Rating
Intact rock strength 15-0
RQD 20-3
Spacing of joints 20-5
Condition of joint 30-0
Ground water 15-0
Total 100-8


Rating was given for joint orientation and depending on its effects on the structures (favorable or unfavorable). It was added toor subtracted from the other ratings. Five rock mass classes were brought out based on total ratings.

Rock Mass Rating Rock Mass Description Class
81-100 very good rock I
61-80 Good rock II
41-60 Fair rock III
21-40 Poor rock IV
Less than 20 Very poor rock V

The parameters are measured and calculated manually. These calculated values are put in the graph developed byBieniawski’s to get the correct information of stand up time and width of opening.
Barton’s rock mass quality, Q (1974):
Barton, Lien and Lunde presented a rock mass quality classification of supports in 1974. The rock mass quality, Q is a function of six parameters each of which has a rating importance, these are;
Rock Quality Designation Index
Number of joint (Jn)
Roughness of weakest joint (Jr)
The degree of alteration or filling along weakest joint (Ja) and,
Two parameter which account for the rock load (SRF) and
Water inflow (Jw)

The calculated Q value is put on the Bieniawski’s graph to estimate the required support system. These calculations take couple of months to arrive at the desired result. The calculations are critical as it involves safety measures; it has to be calculated very carefully.
The present invention minimizes the time taken for the calculations giving rise to accurate results.

Prior Art:
1) CN104217124A: The invention discloses a TBM (Tunnel Boring Machine) construction surrounding rock classification method depending on engineering sample data, which comprises the following steps: determining sensitive parameters of Rc, Kv, alpha and a water penetration quantity of a tunnelling property of a rock; establishing a sample fuzzy clustering model by adopting a fuzzy clustering principle; carrying out clustering analysis on an engineering project sample so as to obtain a sample clustering result; according to the obtained sample clustering result and influence of each parameter on ROP (tunnelling speed), classifying the tunnelling level of a sample into the level I, the level II and the level III; carrying out further refining and finally obtaining TBM construction surrounding rock tunnelling levels. Aiming at the current condition of shortage of a systematic and complete geological prediction and performance prediction implementing method in the existing TBM construction field, the method disclosed by the invention provides a tunnel surrounding rock systematic classification method under the TBM construction condition according to the construction sample data; the method aims to accurately predict TBM tunnelling performance under the specific geological conditions, guide to determine various construction parameters and expect to provide quantitative data for design of a TBM cutter head.
2) CN105046080B: The invention discloses a kind of rock mass quality classification, to understand opencut rock-mass quality, from the country rock of the vertical scope of opencut, ore body roof, four feature locations of ore body and ore body bottom plate, which are sampled, determines a range of opencut quality, compressive strength of rock, cohesion, the index system of internal friction angle and Deformation Module of Rock Mass as evaluation, and form five ranks of single factor test index Rock Mass Classification, it is determined that degree of membership situation of the four feature locations rock mass samples of opencut to five ranks of Rock Mass Classification in certain vertical scope, and softened, to determine the rock-mass quality of the sample, so that it is determined that whole Slope rock mass quality. The present invention can be used for determining four feature locations rock sample quality of open cut in certain limit, so that it is determined that whole Slope rock mass quality.
3) CN109725129A: The invention discloses a kind of TBM tunnel rock mass classification methods, using rock mass basic quality index BQ as criterion, consider rock mass rockiness and Rock Mass Integrality, consider that underground water, crustal stress, the orientation of engineering axis and the syntagmatic of primary structure face occurrence as finishing factor, determine the rock-mass quality of country rock simultaneously;Step 1, according to the Inversion Calculation according to the following formula of the relationship between rock mass saturated uniaxial compressive strength and TBM device parameter, boring parameter and slag charge morphological parameters;Step 2 is calculated according to the relationship between Rock-mass integrity index and TBM device parameter, boring parameter and slag charge morphological parameters. The present invention is based on underground engineering wall rock stage division in existing " Standard for classification of engineering rock masses ", avoid the parameter for being difficult to obtain, according to the compositive relation in TBM work progress between " rock-machine-slag " three, the relationship of index needed for seeking the boring parameter being easy to get, slag charge feature and rock mass strength and integrality realizes the quick fender graded of tunnel in TBM construction.
4) KR20060027451A: RMR classification method in the design of the tunnel has been widely used in order to classify the rock and to determine the pattern of the rocks gem rating. But these RMR classification is bound to be dependent on the judgment of the empirical descriptor to use the variables available hayeoyaman considering the site conditions and classify the rock. It is practically impossible to evaluate all of the factors considering RMR RMR of the rock mass classification methods when utilized in the design phase.
Therefore, the discriminant analysis was performed in order to use only the quantitative element in the design to ensure the possibility RMR classification. The high correlation between the quantitative data of rock strength or RQD is RMR value, when looking at the existing rock segments rock strength and RQD is an important factor for rock classification. Existing RMR rock classification Rock performing a discriminant analysis considering only the rock classification and two variables sorted through the results determined using the rock strength as an independent variable 74.8% when analyzed, to determine with RQD as the independent variable accuracy of 74.3% when analyzed the RMR rock classification was possible to. When the discriminant analysis in consideration with the rock strength and RQD is RMR rock classification it was possible with an accuracy of 82.5%. The overall chance of performing the design phase through the entire RMR elements from existing case studies the design stage, given the 40.3% level sujunim RMR rock classification will be enough just rock strength and RQD.
5) CN108350737A: It is a kind of design mine working method include the following steps:Multiple input parameter of the exploitation for the mine working;First time design iteration is executed to design with the initial ground supporting system of determination;Assess the kinematics stability of the initial ground supporting system design;Determine whether the kinematics stability meets predetermined safety coefficient and initial ground supporting system described in iteration designs until the kinematics stability meets the predetermined safety coefficient again.

RMR calculation is the crucial calculation and cannot be subject to any human error. The manual calculations take months to arrive at the final value while going through the permutation and combination. Also, the input values for the said calculations are acquired by visual inspection of the mining site by personally visit the site. By doing so, the ongoing work gets hamper. All the prior arts mentioned above do not speak about the saving calculation time and also visual inspection time.

Objectives of the Present Invention:
1) The main objective of the present invention is to calculate Q value.
2) Another objective of the present invention is to estimate rock mass rating,
3) Another objective of the present invention is to save the calculation time.
4) Yet another objective of the present invention is to acquire the input values from mining site without interfering with the ongoing mining activity.

SUMMARY OF INVENTION
In one embodiment the present invention is configured to allow a user to develop on a processing means, a system and a method to calculate Rock Mass Rating and Q value in order to estimate the required support system. The calculation tool receives information relating to the various underground rock parameters. The said tool is operatively associated with the underground details information relating to Rock Mass Rating calculation parameters. A rock mass characteristics tool receives information related to geotechnical characteristics of rock mass adjacent to the mining site.

The rock mass characteristics tool herein disclosed estimates a ground type category based on the information relating to the geotechnical characteristics. A ground support system schematic tool operatively associated with the mining and environment details tool and the ground support system schematic tool configured to generate at least one schematic representation of the mining site.

The mining details tool generates on the display system a plurality of input fields to allow the user to use the input system to input information relating to rock mass calculation parameters. The said parameters include:-

1) Strength of intact rock material (MPa)
2) Rock Quality Designation (RQD)
3) Spacing of Discontinuities
4) Condition of Discontinuities
5) Ground water condition
6) Orientation of Discontinuities
This parametric input is acquired by a remotely operated drone(1) having onboard optical and thermal imaging camera(5) and a wireless transceiver(6). The drone is operated by a mobile device(2) as a remote control having application to send and receive commands and data respectively. The data (images both thermal and optical) received by the mobile device is transmitted to the main computer (3) wirelessly. The computer processes the images using image processing techniques (15) and machine learning (16) to extract the above mentioned parametric inputs. The thermal images play a vital role in extraction of inputs from the mining site images.
The rock mass characteristics tool calculates ground stresses in a vicinity of the mining site based on the input information relating to said calculation parameters. The said rock mass characteristics tool generates on the display system a plurality of input fields to allow the user to use the input system to input information relating to geotechnical characteristics of rock mass adjacent the mining site.
The input values are used in the calculation to compute Q value using different probability techniques to arrive at the said final value of Q which is plotted on the graph and RMR report is generated.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS:
Figure 1 illustrates the block diagram of the RMR calculation system
1. Wirelessly operated drone
2. Mobile device configured as remote control to operate and control the drone and its activity
3. Main computer
4. Cloud server
Figure 2 illustrates the image capturing system (drone)
5. Optical camera, thermal camera and a light source arrangement capable of rotating 360? along x-plane and y-plane
6. Wireless transceiver to receive commands and transmit data
7. Onboard processing unit
8. Rechargeable battery to provide power supply to the drone and its onboard system
Figure 3 illustrates mobile device as remote control
9. Up-down-left-right navigation touch buttons to control and navigate the drone
10. Circular touch knob to rotate the camera arrangement in vertical direction (x-plane)
11. Circular touch knob to rotate the camera arrangement in horizonal direction (y-plane)
12. Touch button to capture image by optical as well as thermal camera at once.
13. Display area of the mobile device
Figure 4 illustrates the modules associated with the application running on main computer
14. Acquiring image as input
15. Image processing unit
16. Machine learning tool
17. Calculating Q value and generating RMR report
Figure 5 illustrates the steps of acquiring images and computing the Q value and generating RMR report
Figure 6 illustrates the Bieniawski’s graph plotted by the invented system to get the correct information of stand up time and width of opening for mining.
Figure 7 illustrates a table created by the invented system to summarize the calculated parameters.
Detail Description:
The disclosed system is illustrated in FIG 6 and 7 and may comprise a number of tools or modules that may be utilized to develop ground support designs for mining. The said system may be implemented on a processing means having at least one display system and at least one user input system. When so implemented, the said integrated system includes various tools or modules are displayed on the display system as one or more ‘tabs’ (not shown), thereby allowing a user to use input system to readily access and/or toggle between the various tools or modules during the design process. User input system allows the user to input various data and design criteria into processing means. Thereafter, such information, data, and design criteria may be used by the system to assist the user in developing suitable schematic representation for mining site. In addition, various design details, performance and safety parameters, and design iterations may be presented and displayed on display system during the design process, as will be described in much greater detail herein.
A system has been developed to calculate Rock Mass Rating and Q value to estimate the required support system. The system performs calculations using the inputs provided by the user. The input parametric rating used for the calculations are
• Strength of intact rock material (MPa)
• Rock Quality Designation (RQD)
• Spacing of Discontinuities
• Condition of Discontinuities
• Ground water condition
• Orientation of Discontinuities
Based on the provided ratings, Rock Mass Rating and Rock Mass Quality (Q) is calculated and automatically gets plotted on the graph for visual graphical results and the calculated parameters are summarized by the system in the form of a table. These calculations are accurate than the calculations which are performed manually by trial and error method. These calculated results indicate the actual width of the opening and stand up time in Rock Mass Rating and required support system in Barton’s Q.
The input values required for the said calculations are acquired from the proposed mining site manually. Visiting the site and taking the rock quality readings is a time consuming process. If the individual responsible for taking the readings does not take proper care while entering the mine for readings, involves the risk of accidents. Also, the process of taking readings hampers the normal work at the mining site.
The said input values are acquired by visual inspection of the mining site. In the present embodiment of the invention comprises a remotely operated drone which acquires the images from the proposed mining site and wirelessly transmits it to the main system. The main system is a web application running on a computer processes the images, extracts the required parametric inputs as mentioned above and performs calculations to arrive at the final value of RMR.
The embodiment of the present system has a system and method of capturing optical as well as thermal images at the mining site where these images are communicated to the main station where they are processed to extract the required and necessary parameters to calculate Q value. The method of capturing optical as well thermal images is implemented through a remotely operated drone. These images are later transmitted to the main computer having image processing system, through which parameters are extracted and using the values of those extracted parameters, Q is calculated.
In the embodiment of the present invention, the optical and thermal image capturing system has a drone(1) comprises of the high definition camera, a thermal imaging camera and a light source (5), a wireless transceiver with antenna (6), a rechargeable battery(8) and a processing unit (7). The optical camera, the thermal imaging camera and the light source arrangement have a movement of 360? along x-plane and 360? along y-axis. This enables the camera to take images inside the mining site in any angle as per the requirement. The light source also moves along with the camera for a clearer view of the scene (and since it is dark and light is not available everywhere inside a mine). The thermal imaging camera is used for characterization of the rock. The drone is operated through a remote control system from outside the mining site.
In the embodiment of the present system, the onboard transceiver interfaced with the processing unit (7), transmits image data to the remote station, outside the mine and receives command to perform action. The received commands are processed by the processing unit onboard and the requested action is performed. The rechargeable battery provides necessary power supply to the entire drone and its system.
In the embodiment of the present system, the remote control that controls the drone from outside the mining site is a mobile device (2) comprises of an LCD display (13), processing unit, wireless transceiver, application to operate the drone and internet connectivity.
In the embodiment of the present system, the application on the mobile device that controls the operation of the drone has a display area (13) where live streaming from camera is visualised, button to capture the image (12), touch screen controls to navigate the drone (9), touch screen controls to adjust the position of the camera horizontally(11) and vertically (10).
In the embodiment of the present system, the computer(3) at the remote station is wirelessly connected to the mobile device(2) which operates the drone(1) as remote control. The captured optical and thermal images are wirelessly communicated to the main computer by the mobile device. The minimum configuration necessary for the said main computer to process the said images and calculate Q must have a graphical processing unit and its necessary peripheral, 2 TB hard disk and 16GB RAM. The main computer is also connected to a server where the captured images are stored. These captured images also contribute to the database used for machine learning processes of the system.
In the embodiment of the present system, the web application (Figure 4) on the main computer(3) is distributed into modules which are internally connected to each other in an order of the process involved in processing the optical as well as thermal images and extracting the required and necessary input parameters. The web application has main four modules viz acquiring image as input(14) image processing(cross correlation between optical and thermal images)(15), and machine learning (16). The optical images and thermal images acquired from a mining site are processed and analyzed using image processing techniques. The output of the image processing from optical as well as thermal images is fed to the cross correlation module where the images are analyzed in depth. The image processing involves plurality of module which processes the acquired image and the characteristics of the images are extracted and analyzed. The analyzed result is then fed to the machine learning module where the exacted parametric values are extracted using data set from the server and the output of the cross correlation module. Once the parametric values are available, the system then process for estimation of Q value and generation RMR.
Moreover, the information and data input to the various modules, as well as the calculations or computations performed by each module are automatically transferred or communicated among the various modules, thereby significantly streamlining the development process and significantly reducing the likelihood for errors to be inadvertently introduced during the development process. In addition, satisfactory and unsatisfactory designs may be reflected immediately. More specifically, unsatisfactory or poorly developed ground support system design elements may be indicated in the color red in the ground support schematic, thereby allowing the user to change the design ‘on-the-fly’ until a satisfactory indication (e.g., by use of the color green) is provided.
The entire process from acquiring the image, processing the image and extracting parametric input and calculations is as follows (Figure 5):
1) Targeting the drone to the mining site by remotely operating it from outside the mine,
2) Adjusting the cameras and light arrangement through the remote control mobile device to take optical and thermal image of the desired location,
3) Capturing the images from the mining site from plurality of locations and different positions,
4) Transmitting the images to the remote control device,
5) Retransmitting the images wirelessly from remote control to the main computer.
6) Processing the images at the main computer using image processing techniques,
7) Extracting the parametric inputs required for the calculation of Q value using machine learning techniques using data sets available at the server,
8) Computing the input values with the extracted inputs using probability techniques,
9) Estimating the accurate Q value from permutation combination to arrive at the final Q value.
10) Generating the RMR report for the support system to be implemented at the site.
Many modifications may readily be contemplated by those skilled in the art to which the invention relates. Many further modifications may readily be contemplated. The description set out above is particularly applicable to high rate clarification applications. However, in conventional clarification where the upstream or downstream processes herein described are not used, the teachings according to the invention may have considerable merit and are also applicable. The specific embodiments described, therefore, should be taken as illustrative of the invention only and not as limiting its scope whatsoever.

Advantages:
1) Combination of optical and thermal images gives precise input values rather than using only optical images.
2) The captured image contributes to the data set used for machine learning enhances the decision making capability of the system with the addition of each image.
,CLAIMS:Claims:
We claim,
1) A Method for Rock Mass Characterization and Rock Support System in Mining comprises of
a) Navigating the wirelessly operated drone inside the mining site through a mobile device configured as a remote control,
b) Acquiring the optical and thermal images from plurality of the places,
c) Transmitting the captured image to the main computer,
d) Processing the images and extracting the features of the images,
e) Obtaining input parametric values from the extracted features,
f) Computing the Q value from plurality combinations of the input values and machine learning tools to arrive at the final Q value,
g) Plotting the Q value on the Bieniawski’s graph,
h) Estimating the correct information of stand up time and width of opening for mining, and
i) Generating RMR report.
2) The computation of Q value as claimed in claim 1, is computed from the extracted input values from the captured images wherein the extracted input values are
a) Strength of intact rock material (MPa)
b) Rock Quality Designation (RQD)
c) Spacing of Discontinuities
d) Condition of Discontinuities
e) Ground water condition
f) Orientation of Discontinuities
3) The image processing method as claimed in claim 1, comprises of plurality of inter connected modules in a process sequence and automatically passed from one module to the next module.
4) The machine learning tool as claimed in claim 1 performs the analysis of the computed plurality of Q values with plurality of combination of the input values and the data set available at the system server.
5) A system for Rock Mass Characterization and Rock Support System in Mining comprises of
a) Wirelessly operated drone(1) having optical and thermal camera (5),
b) A mobile device (2) configured for remote control,
c) A main computer (3) having high speed processing power and data storage capacity, and
d) A server (4).
wherein the onboard optical and thermal cameras (5) having 360? of angle of rotation in x-plane and y-plane captures the required and necessary images;
wherein the mobile device (2) navigates and control the operation of the drone (1) ;
wherein the main computer(3) processes the optical and thermal images to extract parametric input values necessary for the computation of Q value;
wherein the server(4) stores the captured images and prepares and updates the data set with the newly stored images for image processing (15) and the process of machine learning (16) for the estimation of the final result.

Dated this 27th day of July, 2020

[Dr. Ushoshi Guha]
IN/PA – 720
Of Lex Regia
For the Apllicant