Probabilistic evaluation of drilling rate index based on a least square support vector machine and Monte Carlo simulation
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Drilling rate index (DRI) is an important index for evaluating the drillability of rock in mining, tunneling, and underground excavation. Various studies have been implemented to predict DRI based on the relationship between DRI and its influence factors. Meanwhile, many uncertainties are associated with the evaluation of DRI because of the complexity and nonlinearity of rock mechanical and physical properties. But the uncertainty has not been considered in previous studies. In this study, a novel method was proposed to evaluate DRI considering the uncertainty through combining the least square support vector machine (LSSVM) and Monte Carlo simulation (MCS). The LSSVM was adopted to map the relationship between DRI and rock strength index. Latin hypercube sampling (LHS) was used to produce the sample sets based on the uncertainty distribution of rock strength index. MCS was utilized to simulate the uncertainty of DRI. The proposed method was verified by three testing examples with the uncertainties. Interaction effects of DRI’s influence factors were analyzed and discussed. The results show the proposed method can evaluate the DRI reasonably. Compared with the determinate method, the proposed method is more rational and scientific and conforms to the rock engineering practice. Interaction effects should be considered while predicting or evaluating the DRI. LSSVM can not only present well the nonlinear relationship between DRI and its influence factors, but also deal with the interaction effects of the DRI’s influence factors. The proposed method provides a scientific tool to predict and evaluate the DRI and its uncertainty.
KeywordsDrilling rate index Rock strength index Uncertainty Least square support vector machine Monte Carlo simulation
The authors gratefully acknowledge financial support from the Program for Innovative Research Team (in Science and Technology) in University of Henan Province (no. 15IRTSTHN029).
- Armaghani DJ, Mohamad ET, Momeni E, Narayanasamy MS (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on main range granite. Bull Eng Geol Environ 74:1–19Google Scholar
- Barton N (2000) TBM tunnelling in jointed and faulted rock. Balkema, RotterdamGoogle Scholar
- Bruland A (1998) Drillability test methods. NTNU, TrondheimGoogle Scholar
- Dahl F, Bruland A, Grov E, Nilsen B (2010) Trademarking the NTNU/SINTEF drillability test indices. Tunnels Tunn Int 44–46Google Scholar
- Ekincioglu G, Altindag R, Sengun N, Demirdag S, Guney A (2013) Investigation of the relationships between drilling rate index (DRI), physico-mechanical properties and specific cutting energy for some carbonates rocks. Rock mechanics for resources, energy & environment, ISRM International Symposium - EUROCK 2013, 23–26 October, Wroclaw, PolandGoogle Scholar
- Luckman PG, Der Kiureghian A, Sitar N (1987) Use of stochastic stability analysis for Bayesian back calculation of pore pressures acting in a cut at failure. Proceedings of the 5th International Conference on Application of Statistics and Probability in Soil and Structure Engineering, University of British Columbia, VancouverGoogle Scholar
- Mollon G, Daniel D, Abdul HS (2011) Probabilistic analysis of pressurized tunnels against face stability using collocation-based stochastic response surface method. Probab Eng Mech 137(4):385–397Google Scholar
- Selmer-Olsen R, Lien R (1960) Bergartens borbarhet og sprengbarhet. Teknisk Ukeblad nr 34: Oslo pp 3–11Google Scholar
- Sievers H (1950) Die Bestimmung des Bohrwiderstandes von Gesteinen. Gluckauf 86(37/38):776–784Google Scholar
- Von Matern N, Hjelmer A (1943) Forsok med pagrus (Tests with chippings), Medelande nr 65, Statens vaginstitut, Stockholm, 65 pp (English summary, pp 56–60)Google Scholar