A study of soft computing models for prediction of longitudinal wave velocity
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ORIGINAL PAPER
A study of soft computing models for prediction of longitudinal wave velocity Jayraj Singh 1 & A. K. Verma 2 & Haider Banka 1 & T. N Singh 3 & Sachin Maheshwar 2
Received: 23 May 2014 / Accepted: 18 September 2015 # Saudi Society for Geosciences 2016
Abstract Genetic algorithm (GA) and support vector machine (SVM) optimization techniques are applied widely in the area of geophysics, civil, biology, mining, and geo-mechanics. Due to its versatility, it is being applied widely in almost every field of engineering. In this paper, the important features of GA and SVM are discussed as well as prediction of longitudinal wave velocity and its advantages over other conventional prediction methods. Longitudinal wave measurement is an indicator of peak particle velocity (PPV) during blasting and is an important parameter to be determined to minimize the damage caused by ground vibrations. The dynamic wave velocity and physico-mechanical properties of rock significantly affect the fracture propagation in rock. GA and SVM models are designed to predict the longitudinal wave velocity induced by ground vibrations. Chaos optimization algorithm has been used in SVM to find the optimal parameters of the model to increase the learning and prediction efficiency. GA model also has been developed and has used an objective function to be minimized. A parametric study for selecting the optimized parameters of GA model was done to select the best value. The mean absolute percentage error for the predicted wave velocity (V) value has been found to be the least (0.258 %) for GA as compared to values obtained by multivariate regression analysis (MVRA),
* A. K. Verma [email protected]; [email protected] 1
Department of Computer Science and Engineering, Indian School of Mines, Dhanbad-04, India
2
Department of Mining Engineering, Indian School of Mines, Dhanbad-04, India
3
Department of Earth Science, Indian Institute of Technology Bombay, Mumbai, India
artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and SVM. Keywords SVM . ANFIS . ANN . Longitudinal wave velocity . Hardness . Porosity
Introduction Utilization of seismic strategies in geotechnical designing is expanding step-by-step to assess long haul security of rock structure. The dynamic characteristic of rocks are generally portrayed and predicted by various methods. A study has been done to inspect rock bolt requirement, impacting the performance of rock by seismic velocity estimation, estimation of overbreak zone derailment around the excavation, and estimation of rock weathering and the deformation of fractured rock mass properties (Price et al. 1970; Young et al. 1985; Hudson et al. 1980; Karpuz and Pasamehmetoglu 1997; Boadu 1997). Few studies have been done to correlate the static rock properties. Rock types, density, hardness, porosity, strength properties, temperature, grain size and shape, confining pressure, etc. are the most significant factors influencing the longitudinal wave velocity. Rock have exp
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