Skip to main content
SpringerLink
Log in

Prediction of blast-induced air overpressure using support vector machine

التنبؤ الانفجار الناجم عن ارتفاع الضغط الجوي باستخدام دعمناقلات ماآينة

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Prediction of blast-induced air overpressure (AOP) is very complicated and intricate due to the number of influencing parameters affecting air wave propagation. In this paper, an attempt has been made to predict the blast-induced AOP by support vector machine (SVM) using maximum charge per delay and distance from blast-face to monitoring station of AOP. To investigate the suitability of this approach, SVM predictions are compared with a generalized predictor equation. Seventy-five air blasts were monitored at different locations around three mines. AOP data sets of two limestone mines are taken for the training and testing of the SVM network as well as to determine site constants for generalized equation. The remaining mine data sets are used for the validation and comparison of AOP.

Abstract

التنبؤ الانفجار الناجم عن ارتفاع الضغط الجوي (اوب) أمر معقد للغاية ومعقدة نظرا لعدد من المعايير التي تؤثر التأثير على الموجات الجوية. في هذه الورقة ، وبذلت محاولة للتنبؤ وقوع الانفجار الناجم عن ارتفاع الضغط الجوي (اوب) عن طريق دعم الموجه آلة (SVM) باستخدام أقصى لكل تهمة التأخير والمسافة من الانفجار ، وجها لمحطة رصد لاوب. للتحقيق في مدى ملاءمتها لهذا النهج ، SVM التنبؤات هي مقارنة مع تنبؤ المعادلة معممة. 75 الانفجارات الجوية قد تم رصدها في مواقع مختلفة حول ثلاثة مناجم. اوب مجموعات البيانات من الحجر الجيري 2 الألغام هي التي اتخذت لتدريب واختبار شبكة SVM وكذلك لتحديد موقع لثوابت المعادلة معممة. ما تبقى من الألغام مجموعات البيانات التي تستخدم للتحقق من صحة والمقارنة اوب.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Cristianini N, Shawe-Taylor NJ (2000) An introduction to support vector machines. Cambridge University Press, Cambridge

    Google Scholar 

  • Feng XT, Zhao H, Li S (2004) Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. Int J Rock Mech Min Sci 41(7):1087–1107

    Article  Google Scholar 

  • ISRM (1992) Suggested method for blast vibration monitoring. Int J Rock Mech Min Sci Geomech Abst 29:145–146

    Article  Google Scholar 

  • John CP (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical report, MSR-TR-98-14, April 21

  • Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure using neural network. Noise Vib Worldw 36(2):7–16

    Article  Google Scholar 

  • Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine—a neural network approach. J Sound Vib 289:711–725

    Article  Google Scholar 

  • Konya CJ, Walter EJ (1990) Surface blast design. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Liu KY, Qiao CS, Tian SF (2004) Design of tunnel shotcrete-bolting support based on a support vector machine approach. Int J Rock Mech Min Sci 41(3):510–511

    Article  Google Scholar 

  • Muller KR, Smola JA, Scholkopf B (1997) Prediction time series with support vector machines, Proceedings of International Conference on Artificial Neural Networks. Lausanne, Switzerland, 999–1004

  • Oriad LL (2002) Explosive engineering, construction vibrations and geotechnology. International Society of Explosives Engineers, Cleveland, p 680

    Google Scholar 

  • Persson PA, Holmberg R, Lee J (1993) Rock blasting and explosives engineering. CRC, Boca Raton, pp 375–377

    Google Scholar 

  • Rajmeny PK, Joshi A, Bhandari S (2006) Blast designing: theory and practice. Himanshu, New Delhi

    Google Scholar 

  • Sawmliana C, Pal Roy P, Singh RK, Singh TN (2007) Blast induced air overpressure and its prediction using artificial neural network. Min Technol 116(2):41–48

    Article  Google Scholar 

  • Schmidt M (1996) Identifying speaker with support vector networks. Interface, ‘96 Proceedings, Sydney

  • Scholkopf B, Burges C, Vapnik V (1995) Extracting support data for a given task. Proceedings of the First International Conference on Knowledge Discovery and Data Mining. AAAI, Menlo Park

    Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Wiss JF, Linehan PW (1978) Control of vibration and air noise from surface coal mines—III. Bureau of Mines, US, Report No. OFR 103 (3)-79, USA

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Kaufmann, San Francisco

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mining Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001, India

    Manoj Khandelwal

  2. Department of Mechanical & Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247 667, India

    P. K. Kankar

Authors
  1. Manoj Khandelwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. K. Kankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj Khandelwal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Khandelwal, M., Kankar, P.K. Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4, 427–433 (2011). https://doi.org/10.1007/s12517-009-0092-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-009-0092-7

Keywords

Search

Navigation