Staff View: Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation

Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation

This paper discusses the various artificial neural techniques used to analyze 285 blasting data set from limestone quarry in Thailand consisting of blast design data and percentage of boulders as blast performance criteria. In the beginning, the data sets have been divided into train and test sets u...

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Main Authors:	Murldhar, Bhatawdekar Ramesh, Mohmad, Edy Tonnizam, Kumar, Abhijeet, Kumar, Sinha Rabindra, Armaghani, Danial Jahed, Pathak, Pranjal, Dan, Firdaus Md
Format:	Article
Published:	Books and Journals Private Ltd. 2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/36093/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123994496&partnerID=40&md5=cbc8a052f94bd946d7689462e2c50aec
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spelling	my.um.eprints.360932024-07-02T04:27:22Z http://eprints.um.edu.my/36093/ Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation Murldhar, Bhatawdekar Ramesh Mohmad, Edy Tonnizam Kumar, Abhijeet Kumar, Sinha Rabindra Armaghani, Danial Jahed Pathak, Pranjal Dan, Firdaus Md TA Engineering (General). Civil engineering (General) This paper discusses the various artificial neural techniques used to analyze 285 blasting data set from limestone quarry in Thailand consisting of blast design data and percentage of boulders as blast performance criteria. In the beginning, the data sets have been divided into train and test sets using genetic algorithm to maintain their statistical properties. Five-fold cross validation technique has been used for the selection of the network configurations and the regularization constant. Step by step analysis of data has been carried out. Four types of models are used for analysis namely - neural networks with whole set of features, neural networks with feature transformation using principal component analysis, neural networks with feature selection using information gain by decision trees and neural networks with feature selection using forward search. Neural network with feature selection using forward search, produced the best results among the four models. However, the model has not been able to produce any significant improvement in the results. The analysis shows that there exists an insignificant correlation and mean square error values with the collected data samples from the blast results of the quarry. The methods to forcibly produce significant mean square error and correlation values, that show apparently good results, have been shown. However, such models are not fit for generalizing the results. These models will not be able to predict the results for new and unnoticed inputs. © 2021, Books and Journals Private Ltd.. All rights reserved. Books and Journals Private Ltd. 2021 Article PeerReviewed Murldhar, Bhatawdekar Ramesh and Mohmad, Edy Tonnizam and Kumar, Abhijeet and Kumar, Sinha Rabindra and Armaghani, Danial Jahed and Pathak, Pranjal and Dan, Firdaus Md (2021) Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation. Journal of Mines, Metals and Fuels, 69 (8). pp. 208-223. ISSN 00222755, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123994496&partnerID=40&md5=cbc8a052f94bd946d7689462e2c50aec
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Murldhar, Bhatawdekar Ramesh Mohmad, Edy Tonnizam Kumar, Abhijeet Kumar, Sinha Rabindra Armaghani, Danial Jahed Pathak, Pranjal Dan, Firdaus Md Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
description	This paper discusses the various artificial neural techniques used to analyze 285 blasting data set from limestone quarry in Thailand consisting of blast design data and percentage of boulders as blast performance criteria. In the beginning, the data sets have been divided into train and test sets using genetic algorithm to maintain their statistical properties. Five-fold cross validation technique has been used for the selection of the network configurations and the regularization constant. Step by step analysis of data has been carried out. Four types of models are used for analysis namely - neural networks with whole set of features, neural networks with feature transformation using principal component analysis, neural networks with feature selection using information gain by decision trees and neural networks with feature selection using forward search. Neural network with feature selection using forward search, produced the best results among the four models. However, the model has not been able to produce any significant improvement in the results. The analysis shows that there exists an insignificant correlation and mean square error values with the collected data samples from the blast results of the quarry. The methods to forcibly produce significant mean square error and correlation values, that show apparently good results, have been shown. However, such models are not fit for generalizing the results. These models will not be able to predict the results for new and unnoticed inputs. © 2021, Books and Journals Private Ltd.. All rights reserved.
format	Article
author	Murldhar, Bhatawdekar Ramesh Mohmad, Edy Tonnizam Kumar, Abhijeet Kumar, Sinha Rabindra Armaghani, Danial Jahed Pathak, Pranjal Dan, Firdaus Md
author_facet	Murldhar, Bhatawdekar Ramesh Mohmad, Edy Tonnizam Kumar, Abhijeet Kumar, Sinha Rabindra Armaghani, Danial Jahed Pathak, Pranjal Dan, Firdaus Md
author_sort	Murldhar, Bhatawdekar Ramesh
title	Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
title_short	Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
title_full	Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
title_fullStr	Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
title_full_unstemmed	Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
title_sort	prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation
publisher	Books and Journals Private Ltd.
publishDate	2021
url	http://eprints.um.edu.my/36093/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123994496&partnerID=40&md5=cbc8a052f94bd946d7689462e2c50aec
_version_	1805881095847149568
score	13.211869

Prediction of boulder generation in opencast bench blasting using artificial neural network and their limitation

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