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

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.

A Comparative Study of Two Tree-Based Models for Predicting Flyrock Velocity at Open Pit Bench Mining

“…Another conducted study highlights the considerable influence of powder factor and blast ability index on flyrock behavior, pinpointing the consideration of site-specific geological condition as a key factor in assessing the distance of flyrock produced by blasting. They also clearly stated that their finding cannot be trustable for other mining sites [32].…”

Assessment of the ground vibration during blasting in mining projects using different computational approaches

“…Fissha et al 88 introduced the application of Bayesian-based ANN to improve the Mikurahana quarry blasting impact, demonstrating a new advantage of neural networks for ground improvement. Bhatawdekar et al 89 developed a soft computing model for estimating fly rock distance using different input variables, such as hole diameter, burden, stemming length, rock density, charge-per-meter, powder factor (PF), blast-ability index (BI), and weathering index datasets using hybrid ANN approaches. This study used an ANN technique with Bayesian Regularization algorithms to construct a prediction model for PPV.…”

High-Speed Motion Analysis-Based Machine Learning Models for Prediction and Simulation of Flyrock in Surface Mines

“…The study used a dataset of 82 bench blasts and compared the results with those from multiple linear regression (MLR) models to discover that ORELM models were more efficient for predicting flyrock than both ANN and MLR models. Further, Bhatawdekar et al [37] applied a novel equilibrium optimization technique to the ELM to anticipate the extent of flyrock generated in 114 blasts conducted at an aggregate limestone quarry in Thailand. In addition, two hybrid prediction models, namely, PSO-ANN and PSO-ELM, were developed and all models were run ten times.…”