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Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasti...

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Main Authors:	Koopialipoor, Mohammadreza, Fallah, Ali, Armaghani, Danial Jahed, Azizi, Aydin, Mohamad, Edy Tonnizam
Format:	Article
Published:	Springer London 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/88582/ http://dx.doi.org/10.1007/s00366-018-0596-4
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spelling	my.utm.885822020-12-15T10:31:22Z http://eprints.utm.my/id/eprint/88582/ Three hybrid intelligent models in estimating flyrock distance resulting from blasting Koopialipoor, Mohammadreza Fallah, Ali Armaghani, Danial Jahed Azizi, Aydin Mohamad, Edy Tonnizam TA Engineering (General). Civil engineering (General) Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others. Springer London 2019-01-09 Article PeerReviewed Koopialipoor, Mohammadreza and Fallah, Ali and Armaghani, Danial Jahed and Azizi, Aydin and Mohamad, Edy Tonnizam (2019) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Engineering with Computers, 35 (1). pp. 243-256. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-018-0596-4 DOI:10.1007/s00366-018-0596-4
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Koopialipoor, Mohammadreza Fallah, Ali Armaghani, Danial Jahed Azizi, Aydin Mohamad, Edy Tonnizam Three hybrid intelligent models in estimating flyrock distance resulting from blasting
description	Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.
format	Article
author	Koopialipoor, Mohammadreza Fallah, Ali Armaghani, Danial Jahed Azizi, Aydin Mohamad, Edy Tonnizam
author_facet	Koopialipoor, Mohammadreza Fallah, Ali Armaghani, Danial Jahed Azizi, Aydin Mohamad, Edy Tonnizam
author_sort	Koopialipoor, Mohammadreza
title	Three hybrid intelligent models in estimating flyrock distance resulting from blasting
title_short	Three hybrid intelligent models in estimating flyrock distance resulting from blasting
title_full	Three hybrid intelligent models in estimating flyrock distance resulting from blasting
title_fullStr	Three hybrid intelligent models in estimating flyrock distance resulting from blasting
title_full_unstemmed	Three hybrid intelligent models in estimating flyrock distance resulting from blasting
title_sort	three hybrid intelligent models in estimating flyrock distance resulting from blasting
publisher	Springer London
publishDate	2019
url	http://eprints.utm.my/id/eprint/88582/ http://dx.doi.org/10.1007/s00366-018-0596-4
_version_	1687393591225221120
score	13.211869

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

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