NDLI: Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS

Content Provider	Springer Nature Link
Author	Trivedi, Ratnesh Singh, T. N. Gupta, Neel
Copyright Year	2015
Abstract	The aim of present study is prediction of blast-induced flyrock distance in opencast limestone mines using artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). Blast design and geotechnical variables such as linear charge concentration, burden, stemming length, specific charge, unconfined compressive strength, and rock quality designation have been selected as independent variables and flyrock distance has been used as dependent variable. Blasts required for the study purpose have been conducted in four limestone mines in India. Out of one hundred and twenty-five (125) blasts, dataset of one hundred blasts have been used for training, testing and validation of the ANN and ANFIS based prediction model. Twenty-five (25) data have been used for evaluation of the trained ANN and ANFIS models. In order to know the relationship among the independent and dependent variables, multi-variable regression analysis (MVRA) has also been performed. The performance indices such as root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) have been evaluated for ANN, ANFIS and MVRA. RMSE as well as MAE have been found lower and R2 has been found higher in case of ANFIS in comparison of ANN and MVRA. ANFIS has been found a superior predictive technique in comparison to ANN and MVRA. Sensitivity analysis has also been performed using ANFIS to assess the impact of independent variables on flyrock distance.
Starting Page	875
Ending Page	891
Page Count	17
File Format	PDF
ISSN	09603182
Journal	Geotechnical and Geological Engineering
Volume Number	33
Issue Number	4
e-ISSN	15731529
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-03-28
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network Adaptive neuro fuzzy inference system Blasting Opencast mining Flyrock distance Geotechnical Engineering & Applied Earth Sciences Hydrogeology Terrestrial Pollution Waste Management/Waste Technology Civil Engineering
Content Type	Text
Resource Type	Article
Subject	Soil Science Geology Architecture Geotechnical Engineering and Engineering Geology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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