prediction of rock quality designation using inductive modeling techniques

1. PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 1

2. FINAL YEAR RESEARCH PROJECT
PROPOSAL PRESENTATION
PROJECT MEMBERS:
IBRAHIM KHAN 16PWMIN0776
WASEEM ANWAR 16PWMIN0778
Supervisor:
Engr. Sajjad Hussain
2PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES

3. TITLE:
PREDICTION OF ROCK QUALITY DESIGNATION BY
USING INDUCTIVE MODELING TECHNIQUES
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 3

4. Contents
 Introduction
 Problem statement
 Aims & Objectives
 Proposed research Methodology
 Justifications of Research
 References
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 4

5. INTRODUCTION
 Rock quality designation (RQD) is a rock mass
classification system used for qualitative classification of
rock mass environment.
 This system was developed by Deere et al. in 1964.
 RQD is determined/estimated by two methods i.e direct
and indirect
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 5

6. Direct method
 RQD is an index expressed in percentage and evaluated on the
basis of measuring rock core pieces with a length greater than
10 cm along the core drill hole as shown in figure 1.
 The size of rock core is proposed to be minimum 54.7 mm (2.15
inches) in diameter and should be drilled with a double-tube
core barrel.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 6

7. Direct method
 RQD =
𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑐𝑜𝑟𝑒 𝑝𝑖𝑒𝑐𝑖𝑒𝑠 > 10𝑐𝑚 𝑙𝑒𝑛𝑔ℎ𝑡
𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑟𝑒 𝑙𝑒𝑛𝑔ℎ𝑡 𝑜𝑓 𝑐𝑜𝑟𝑒 𝑟𝑢𝑛
x 100
 Figure 1. RQD determination procedure
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 7

8. Table 1: Rock quality designation (RQD) classification
index
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 8

9. Indirect Method
 The direct estimation of RQD value is very time consuming, cost
effective.
 Further the RQD value is considered zero for very weak rock mass which
directly underestimate the RMR and Q-System value.
 As compare to direct method indirect methods are economical to be used
for estimation of RQD.
 Therefore most of the researcher focus on indirect estimation of RQD
value.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 9

10. Indirect Method
 Indirect method Includes:
 Empirical models,
 Multi-linear regression models
 Artificial Neural Network Modelling techniques are used for
estimation of RQD for given rock mass condition.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 10

11. Indirect Method
 In this project the Value of RQD will be estimated using
different estimation techniques likes empirical models,
Multi-linear regression and Artificial Neural Network
Modelling techniques.
 Detail comparative analysis will be carried out for
selection of the most appropriate model for given rock
mass condition for estimation of RQD.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 11

12. PROBLEM STATEMENT
 The direct estimation of RQD value is very time consuming, cost
effective. Further the RQD value is considered zero for very
weak rock mass which underestimate the RMR and Q-System
value. As compare to direct method indirect methods are
economical to be used for estimation of RQD.
 Therefore it is very important to estimate RQD value using
indirect estimation techniques.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 12

13. AIMS:
 To predict RQD using inductive modelling techniques
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 13

14. OBJECTIVE:
The proposed objectives of the project are:
 To estimate RQD using different empirical model.
 To predict RQD using multi linear regression modeling
technique.
 To predict RQD using ANN technique.
 To select the most appropriate model as alternative estimation
techniques for RQD.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 14

15. METHODOLOGY
METHODOLOGY
The Methodology for this research work is divided into the following
steps:
 Extensive literature survey about rock mass characterization, RQD
direct and indirect determinations, Empirical Models, MLRM, ANN will
be carried out.
 Borehole data of any hydropower project will be collected for
determination of RQD
 Estimation of RQD using different available empirical models will be
carried out
 Prediction of RQD using MLRM will be carried out
 Prediction of RQD using ANN will be carried out
 Comparative analysis of different estimation/predicting methods for
RQD will be carried out
 A proper estimation method/model for RQD will be selected
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 15

16. Justifications of the Research
METHODOLOGY
The proposed research work will be helpful in the followings:
 Prediction and evaluation of RQD for given rock mass
environment through different indirect methods.
 Selection of the most suitable and appropriate model
for RQD estimation
 Provide a common basis for effective communication
amongst all the personnel concerned with geotechnical
aspects of the project
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 16

17. REFERENCE
[1]Bahrami, S., Doulati Ardejani, F., & Baafi, E. (2016). Application of artificial
neural network coupled with genetic algorithm and simulated annealing to solve
groundwater inflow problem to an advancing open pit mine. Journal of
Hydrology, 536(1), 471–484. https://doi.org/10.1016/j.jhydrol.2016.03.002
[2]Hussain, S., Mohammad, N., Khan, M., Rehman, Z. U., & Tahir, M. (2016).
Comparative Analysis of Rock Mass Rating Prediction Using Different Inductive
Modeling Techniques. 5(1), 9–15. https://doi.org/10.5923/j.mining.20160501.02
[3]M, W. E. (2013). The Usefulness of Rock Quality Designation (RQD) in
Determining Strength of the Rock. International Refereed Journal of Engineering
and Science (IRJES) ISSN (Online), 2(9), 2319–183.
PREDICTION OF ROCK QUALITY DESIGNATION BY USING INDUCTIVE MODELING TECHNIQUES 17