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

[1] 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

[2] lvarez-Vigil A. E., 2020 Predicting Blasting Propagation Velocity and Vibration Frequency Using Artificial Neural Networks Search in Google Scholar

[3] 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

[4] 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

[5] 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

[6] 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

[7] 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

[8] 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

[9] 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

[10] 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

[11] 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

[12] 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

[13] 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

[14] Dusenberry D., 2010 Handbook for Blast Resistant Design of Buildings, pp. 8-9 Search in Google Scholar

[15] 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

[16] Ghosh A., Daemen J.K., 2004 A Simple New Blast Vibration Predictor. In: Proc. 24^th US Symp. Rock Mechanics, Texas, USA, pp.151–161 Search in Google Scholar

[17] 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

[18] Hemant A., Pradeep T., 2020 Blast Vibration Dependence on Total Explosives Weight in Open-Pit Blasting Search in Google Scholar

[19] 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

[20] 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

[21] Hoang Nguyen, 2019 Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques Search in Google Scholar

[22] 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

[23] 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

[24] 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

[25] 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

[26] 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

[27] 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

[28] 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

[29] 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

[30] 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

[31] 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

[32] 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

[33] 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

[34] Lima Xingping Lai, 2019 Blast-Casting Mechanism and Parameter Optimization of a Benched Deep-Hole in an Opencast Coal Mine Search in Google Scholar

[35] Matidz Mulalo, 2017 Assessment of Blast-Induced Ground Vibration at Jinduicheng Molybdenum Open Pit Mine Search in Google Scholar

[36] McKenzie C., 2008 Quarry Blast Monitoring Technical and Environmental Perspective, Quarry Management, p. 23 Search in Google Scholar

[37] 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

[38] 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

[39] 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

[40] 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

[41] 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