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Intelligent Technique for Prediction of Blast Fragmentation Due to the Blasting in Tropically Weathered Limestone

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Proceedings of Geotechnical Challenges in Mining, Tunneling and Underground Infrastructures (ICGMTU 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 228))

Abstract

In the rock blasting scenario, the success of fragmentation plays a major role. Prediction of blasted rock mass fragmentation has a significant role in the overall economics of opencast mines. Blast fragmentation has a direct impact on efficiency and cost of operation consisting of loading, transport and crushing. Tropical weathering has a direct impact on rock mass properties and strength of rock. Thus, challenging issues are created for blastability of tropically weathered limestone.

It is necessary to analyze the blast fragmentation and optimize the blasting parameters. Selected limestone mine for this study is in Thailand. Various rock mass properties such as GSI, RMR, and stemming length were studied to find out the correlation with rock fragmentation. Stiffness ratio, hole diameter to burden ratio, powder factor, maximum charge per delay, RQD, blastability index (BI), weathering index (WI) and mean block size were input parameters to analyse mean rock fragment size.

Total 105 data sets were collected. In this paper, a hybrid model with Artificial neural network (ANN), Particle swarm optimization (PSO) known as PSO-ANN was implemented to analyse the blast fragmentation. Multivariate regression Analysis (MVRA) was also performed. 83 datasets were trained and balance data sets were utilised for testing data. R2 values for training with PSO-ANN and MVRA showed 0.818 and 0.657 respectively. R2 values for testing with PSO-ANN and MVRA showed 0.70 and 0.694 respectively. Thus the hybrid model PSO-ANN was found useful to predict fragmentation.

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Author information

Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India

    Ramesh Murlidhar Bhatawdekar & Devanshu Pathak

  2. Geotropik, Department of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ramesh Murlidhar Bhatawdekar & Edy Tonnizam Mohamad

  3. Department of Civil Engineering, National Institute of Technology, Patna, India

    Deepak Kumar

  4. Siam City Concrete Company Limited, Bangkok, Thailand

    Saksarid Changtham

  5. Earth Science Department, Indian Institute of Technology, Mumbai, 400 076, India

    Singh TrilokNath

Authors
  1. Ramesh Murlidhar Bhatawdekar
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  2. Deepak Kumar
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  3. Saksarid Changtham
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  4. Devanshu Pathak
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  5. Singh TrilokNath
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  6. Edy Tonnizam Mohamad
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Corresponding author

Correspondence to Devanshu Pathak .

Editor information

Editors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Amit Kumar Verma

  2. Geotropik Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Edy Tonnizam Mohamad

  3. Innovative Consultancy and Research Pte. Ltd.,, Singapore, Singapore

    Ramesh Murlidhar Bhatawdekar

  4. Central Institute of Mining and Fuel Research, Nagpur, India

    Avtar Krishen Raina

  5. School of Engineering, Information Technology and Physical Sciences, Federation University, Ballarat, VIC, Australia

    Manoj Khandelwal

  6. Department of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Danial Armaghani

  7. Department of Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad, Dhanbad, India

    Kripamoy Sarkar

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Bhatawdekar, R.M., Kumar, D., Changtham, S., Pathak, D., TrilokNath, S., Mohamad, E.T. (2022). Intelligent Technique for Prediction of Blast Fragmentation Due to the Blasting in Tropically Weathered Limestone. In: Verma, A.K., et al. Proceedings of Geotechnical Challenges in Mining, Tunneling and Underground Infrastructures. ICGMTU 2021. Lecture Notes in Civil Engineering, vol 228. Springer, Singapore. https://doi.org/10.1007/978-981-16-9770-8_53

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  • DOI: https://doi.org/10.1007/978-981-16-9770-8_53

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9769-2

  • Online ISBN: 978-981-16-9770-8

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