NDLI: Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

Content Provider	Springer Nature Link
Author	Jahed Armaghani, D. Tonnizam Mohamad, E. Hajihassani, M. Alavi Nezhad Khalil Abad, S. V. Marto, A. Moghaddam, M. R.
Copyright Year	2015
Abstract	Mines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R 2), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R 2 values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock.
Starting Page	109
Ending Page	121
Page Count	13
File Format	PDF
ISSN	01770667
Journal	Engineering with Computers
Volume Number	32
Issue Number	1
e-ISSN	14355663
Language	English
Publisher	Springer London
Publisher Date	2015-03-20
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Blasting Flyrock Empirical graph Artificial neural network Adaptive neuro-fuzzy inference system Computer-Aided Engineering (CAD, CAE) and Design Math. Applications in Chemistry Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Mechanics ApplicationMathematics/Computational Methods of Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering Computer Science Applications Modeling and Simulation Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Prediction of blast-induced flyrock using differential evolution algorithm

Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Several non-linear models in estimating air-overpressure resulting from mine blasting

Prediction of rock fragmentation due to blasting using artificial neural network

Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting

Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model

A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting

Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

Similar Documents

Prediction of blast-induced flyrock using differential evolution algorithm

Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Several non-linear models in estimating air-overpressure resulting from mine blasting

Prediction of rock fragmentation due to blasting using artificial neural network

Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting

Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model

A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting

Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods