Estimating Flyrock Distance Induced Due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer

Ramesh Murlidhar Bhatawdekar, Radhikesh Kumar (), Mohanad Muayad Sabri Sabri, Bishwajit Roy, Edy Tonnizam Mohamad, Deepak Kumar and Sangki Kwon
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Ramesh Murlidhar Bhatawdekar: Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Radhikesh Kumar: Department of Computer Science and Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna 800005, India
Mohanad Muayad Sabri Sabri: Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Bishwajit Roy: School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India
Edy Tonnizam Mohamad: Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Deepak Kumar: Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna 800005, India
Sangki Kwon: Department of Energy Resources Engineering, Inha University, Yong-Hyun Dong, Nam Ku, Incheon 402-751, Republic of Korea

Sustainability, 2023, vol. 15, issue 4, 1-26

Abstract: Blasting is essential for breaking hard rock in opencast mines and tunneling projects. It creates an adverse impact on flyrock. Thus, it is essential to forecast flyrock to minimize the environmental effects. The objective of this study is to forecast/estimate the amount of flyrock produced during blasting by applying three creative composite intelligent models: equilibrium optimizer-coupled extreme learning machine (EO-ELM), particle swarm optimization-based extreme learning machine (PSO-ELM), and particle swarm optimization-artificial neural network (PSO-ANN). To obtain a successful conclusion, we considered 114 blasting data parameters consisting of eight inputs (hole diameter, burden, stemming length, rock density, charge-per-meter, powder factor (PF), blastability index (BI), and weathering index), and one output parameter (flyrock distance). We then compared the results of different models using seven different performance indices. Every predictive model accomplished the results comparable with the measured values of flyrock. To show the effectiveness of the developed EO-ELM, the result from each model run 10-times is compared. The average result shows that the EO-ELM model in testing (R2 = 0.97, RMSE = 32.14, MAE = 19.78, MAPE = 20.37, NSE = 0.93, VAF = 93.97, A20 = 0.57) achieved a better performance as compared to the PSO-ANN model (R2 = 0.87, RMSE = 64.44, MAE = 36.02, MAPE = 29.96, NSE = 0.72, VAF = 74.72, A20 = 0.33) and PSO-ELM model (R2 = 0.88, RMSE = 48.55, MAE = 26.97, MAPE = 26.71, NSE = 0.84, VAF = 84.84, A20 = 0.51). Further, a non-parametric test is performed to assess the performance of these three models developed. It shows that the EO-ELM performed better in the prediction of flyrock compared to PSO-ELM and PSO-ANN. We did sensitivity analysis by introducing a new parameter, WI. Input parameters, PF and BI, showed the highest sensitivity with 0.98 each.

Keywords: flyrock; weathering index (WI); equilibrium optimizer (EO); particle swarm optimization (PSO); extreme learning machine (ELM); artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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