A Matlab-Based Neural Network Model for Predicting Blast-Induced Ground Vibration

Pradeep, T.; Sri Chandrahas, N.; Fissha, Yewuhalashet; Sravan Kumar, K.; Raghavendra, K.P.

doi:10.2478/minrv-2024-0029

Abstract

This research delves into using an artificial neural network (ANN) to forecast blast-induced ground vibration, vital for controlling the impact of blasting on nearby residential areas. By leveraging data from Singareni mines, the ANN model incorporates various input parameters to predict ground vibration intensity (peak particle velocity). With a dataset of 150 entries and sensitivity analysis, the ANN demonstrates a robust regression coefficient of 0.92, signifying its predictive strength. Comparative analysis favors the ANN model, showcasing its potential in mitigating adverse effects on residential zones, marking a significant stride in managing blast-induced ground vibration prediction using ANN.

References

Abiodun Ismail Lawala, Musa Adebayo Idrisa, 2021. An Artificial Neural Network-Based Mathematical Model for the Prediction of Blast-Induced Ground Vibrations
Search in Google Scholar Back to article
lvarez-Vigil A. E., 2020 Predicting Blasting Propagation Velocity and Vibration Frequency Using Artificial Neural Networks
Search in Google Scholar Back to article
Anand Kumar, Pusker Singh, Sanjay Kumar Sharma, Nawal Kishore C. S. Singh, 2021. Quantitative Assessment of BIGV and Structural Response Based on Velocity and Frequency around an Opencast Mine, Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi 221 005, India
Search in Google Scholar Back to article
Sri Chandrahas N., Choudhary B.S., Venkataramayya M.S., 2023 Firing Pattern and Spacing Burden Ratio Selection in Jointed Overburden Benches Using Unmanned Aerial Vehicle and Artificial Intelligence Based Tool, Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023). DOI: http://10.2991/978-94-6463-252-1_134
Search in Google Scholar Back to article
Chandrahas N.S., Fissha Y., Choudhary B.S., Olamide Taiwo B., Venkataramayya M.S., Adachi T., 2024 Experimental Data – Driven Algorithm to Predict Muckpile Characteristics in Jointed Overburden Bench Using Unmanned Aerial Vehicle and AI Tools. International Journal of Mining, Reclamation and Environment, 1–35. https://doi.org/10.1080/17480930.2024.2340876
Search in Google Scholar Back to article
Mishra A.K., 2019 An Innovative Technique of Simplified Signature Hole Analysis for Prediction of Blast-Induced Ground Vibration of Multi-Hole/ Production Blast: An Empirical Analysis.
Search in Google Scholar Back to article
Bansingh Z., 2019. Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization.
Search in Google Scholar Back to article
Jiang L., Zeng J., Wang G., 2019 A Discrete Dynamic Response Model with Multiple Degrees of Freedom for Horizontal Goaf Group, J. Rock Mech. Eng., pp. 35, 59–67
Search in Google Scholar Back to article
Tileylioglu S., Stewart J.P., Nigbor R.L., 2011 Dynamic Stiffness and Damping of a Shallow Foundation from Forced Vibration of a Field Test Structure. J. Geotech. Geoenviron. Eng., 137, 344–353. [CrossRef].
Search in Google Scholar Back to article
Nielsen A.H., 2009. On the Use of Rayleigh Damping for Seismic Analysis. Proc. Inst. Civ. Eng. Eng. Comput. Mech., 162, 215–220. [CrossRef]
Search in Google Scholar Back to article
Choudhary B.S., Mishra A.K., 2021 Modeling the Effects of Ground Vibrations on the Surface due to Blasting in Underground Coal Mines
Search in Google Scholar Back to article
Armaghani D.J., Kumar D., 2020. A Novel Approach for Forecasting of Ground Vibrations Resulting from Blasting: Modified Particle Swarm Optimization Coupled Extreme Learning Machine
Search in Google Scholar Back to article
Duvall W.I., Fogleson D.E., 2007 Review of Criteria for Estimating Damage Toresidences from Blasting Vibrations. USBM, RI, vol. 5968, p. 19
Search in Google Scholar Back to article
Dusenberry D., 2010 Handbook for Blast Resistant Design of Buildings, pp. 8-9
Search in Google Scholar Back to article
Enayatollahi I., Aghajani Bazzazi A., Asadi A., 2014. Comparison between Neural Networks and Multiple Regression Analysis to Predict Rock Fragmentation in Open-Pit Mines. Rock Mechanics and Rock Engineering, Volume 47, Issue2, pp. 799 – 807
Search in Google Scholar Back to article
Ghosh A., Daemen J.K., 2004 A Simple New Blast Vibration Predictor. In: Proc. 24th US Symp. Rock Mechanics, Texas, USA, pp.151–161
Search in Google Scholar Back to article
Ghosh S.I., 1973. Criteria for Safety and Design of Structures Subjected to Underground Blast. Bureau of Indian Standard. ISI Bulletin IS-6922
Search in Google Scholar Back to article
Hemant A., Pradeep T., 2020 Blast Vibration Dependence on Total Explosives Weight in Open-Pit Blasting
Search in Google Scholar Back to article
Sri Chandrahas N., Choudhary B.S., Krishna Prasad N.S.R., Musunuri V., Rao K.K., 2021 An Investigation into the Effect of Rockmass Properties on Mean Fragmentation. Arch. Min. Sci., 66, 561–578
Search in Google Scholar Back to article
Sri Chandrahas N., Choudhary B.S., Vishnu Teja M., Venkataramayya M.S., Krishna Prasad N.S.R., 2022 XG Boost Algorithm to Simultaneous Prediction of Rock Fragmentation and Induced Ground Vibration Using Unique Blast Data. Appl. Sci., 12(10), 5269; DOI: https://doi.org/10.3390/app12105269
Search in Google Scholar Back to article
Hoang Nguyen, 2019 Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques
Search in Google Scholar Back to article
Hoang Nguyen, Xuan-Nam Bui B.C., 2020. Soft Computing Models for Predicting Blast-Induced Air Over-Pressure: A Novel Artificial Intelligence Approach
Search in Google Scholar Back to article
Huay Kaepty, 2019 Monitoring and Control Airblast Overpressures in an Open Pit Coal Mine Jaroonpattanapong, K. Tachom, Department of Mining and Petroleum Engineering, Faculty of Engineering, Chiang Mai University
Search in Google Scholar Back to article
Smith A. et al., 2018. Impact of Ground Vibration on Residential Buildings: A Case Study, Journal of Structural Engineering, 25(3), 112-125
Search in Google Scholar Back to article
Jones B. et al., 2016 Assessment of Structural Damage Due to Ground Vibration in Residential Areas, Construction and Building Materials, 40, 225-234
Search in Google Scholar Back to article
Brown C., Smith D., 2019 Effects of Ground Vibration on Human Health: A Review, Environmental Health Perspectives, 127(4), 460-471
Search in Google Scholar Back to article
Johnson E. et al., 2020 Economic Impacts of Ground Vibration on Residential Property Values, Journal of Real Estate Economics, 35(2), 201-215
Search in Google Scholar Back to article
Garcia R. et al., 2017 Effects of Ground Vibration on Sensitive Equipment: A Case Study in a Residential Area, Journal of Environmental Engineering, 30(4), pp. 287-299
Search in Google Scholar Back to article
Jahed Armaghani Marto, 2013 Blasting Induced Flyrock and Ground Vibration Prediction through an Expert Artificial Neural Network Based on Particle Sparm Optimization
Search in Google Scholar Back to article
Cristianini N., Shawe-Taylor J., 2000 An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, London
Search in Google Scholar Back to article
Sri Chandrahas N., Choudhary B.S., Venkataramayya M.S., 2018 Identification of Most Influencing Blast Design Parameters on Mean Fragmentation Size and Muckpile by Principal Component Analysis. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume 8, Issue 2S2
Search in Google Scholar Back to article
Jian Zhou A., Yingui Qiu A., Manoj Khandelwal B., 2016 Developing a Hybrid Model of Jaya Algorithm-Based Extreme Gradient Boosting Machine to Estimate Blast-Induced Ground Vibrations
Search in Google Scholar Back to article
Khandelwal M., 2014 Artificial Neural Network as a Tool for Back Break Prediction. Geotechnical and Geological Engineering, 32(1), 21-30
Search in Google Scholar Back to article
Lima Xingping Lai, 2019 Blast-Casting Mechanism and Parameter Optimization of a Benched Deep-Hole in an Opencast Coal Mine
Search in Google Scholar Back to article
Matidz Mulalo, 2017 Assessment of Blast-Induced Ground Vibration at Jinduicheng Molybdenum Open Pit Mine
Search in Google Scholar Back to article
McKenzie C., 2008 Quarry Blast Monitoring Technical and Environmental Perspective, Quarry Management, p. 23
Search in Google Scholar Back to article
Mostafa T.M.., 2009 Artificial Neural Network for Prediction and Control of Blasting Vibrations in Assiut (Egypt) Limestone Quarry, IntJRockMechMinSci46(2):426–431
Search in Google Scholar Back to article
Monjezi M., Bahrami A., Yazdian Varjani A., 2010 Simultaneous Prediction of Fragmentation and Fly Rock in Blasting Operation Using Artificial Neural Networks, International Journal of Rock Mechanics and Mining Sciences, Volume 47, Issue 3, pp.476 – 480
Search in Google Scholar Back to article
Monjezi M., Amiri H., Farrokhi A., Goshtasbi K., 2010. Prediction of Rock Fragmentation due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks, Geotechnical and Geological Engineering, 28(4), 423-430
Search in Google Scholar Back to article
Taiwo B.O., Yewuhalashet F., Adamolekun L.B., Bidemi O.O., Famobuwa O.V., Victoria A.O., 2023 Development of Artificial Neural Network Based Mathematical Models for Predicting Small Scale Quarry Powder Factor for Efficient Fragmentation Coupled with Uniformity Index Model, Artificial Intelligence Review, 0123456789 https://doi.org/10.1007/s10462-023-10524-1
Search in Google Scholar Back to article
Sri Chandrahas N., Choudhary B.S., Venkataramayya M.S., 2023 Competitive Algorithm to Balance and Predict Blasting Outcomes Using Measured Field Data Sets, Comput Geosci. https://doi.org/10.1007/s10596-023-10254-x.
Search in Google Scholar Back to article

A Matlab-Based Neural Network Model for Predicting Blast-Induced Ground Vibration

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