Skip to main content
SpringerLink
Log in

Ripping Production Prediction in Different Weathering Zones According to Field Data

  • Original paper
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

In response to the environmental restrictions and the blasting problems, ripping method as a surface excavation method is the most commonly-used in construction of many civil engineering systems. So, it is essential to provide a more applicable rippability model that can effectively predict ripping production (Q) in the field. This paper presents several new models/equations for prediction of Q in diverse weathering zones (grade from II to V) based on field observations and in situ tests. To do this, four sites in Johor state, Malaysia were selected and a total of 123 direct ripping tests were carried out on two types of sedimentary rocks, namely, sandstone and shale. Based on literature’s suggestions and possible conducted field works, point load strength index, sonic velocity, Schmidt hammer rebound number and joint spacing were chosen to estimate Q in different weathering zones. Then, simple and multiple regression analyses, namely linear multiple regression (LMR) and non-linear multiple regression (NLMR) were performed to predict Q. The simple regression analysis generally showed an acceptable and meaningful correlation between the Q and input variables. Additionally, a range of 0.582–0.966 was obtained for coefficient of determination (R2) values of developed LMR models while this range was observed from 0.586 to 0.949 for proposed NLMR models. As a result, both the LMR and NLMR models deliver almost the same predictive performance in estimating the Q for various weathering zones. Nevertheless, in most of the cases, NLMR models can provide higher performance prediction in estimating Q compared to LMR models.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Abdullatif OM, Cruden DM (1983) The relationship between rock mass quality and ease of excavation. Bull Int Assoc Eng Geol 28:183–187

    Article  Google Scholar 

  • Anon (1987) Specifications and application handbook, 10th edn. Komatsu Ltd., Tokyo

    Google Scholar 

  • Atkinson T (1971) Selection of open pit excavating and loading equipment. Trans Inst Min Metall 80:A101–A129

    Google Scholar 

  • Bailey AD (1975) Rock types and seismic velocity versus rippability. Highw Geol Symp Proc 26:135–142

    Google Scholar 

  • Basarir H, Karpuz C (2004) A rippability classification system for marls in lignite mines. Eng Geol 74(3):303–318

    Article  Google Scholar 

  • Basarir H, Karpuz C, Tutluoglu L (2007) A fuzzy logic based rippability classification system. J South Afr Inst Min Metall 107(12):817

    Google Scholar 

  • Basarir H, Karpuz C, Tutluoğlu L (2008) 3D modeling of ripping process. Internat J Geomech 8(1):11–19

    Article  Google Scholar 

  • Bozdag T (1988) Indirect rippability assessment of coal measure rocks. Ankara, Turkey, METU: 86

  • Bradybrooke JC (1988) The state of art of rock cuttability and rippability prediction. In: Proceedings of fifth Australia-New Zealand conference on geomechanics. Sydney, pp 13–42

  • Caterpillar TC (1985) Caterpillar performance handbook, vol 2, 16th edn. Caterpillar Inc., Peoria

    Google Scholar 

  • Caterpillar TC (2001) Caterpillar performance handbook. Caterpillar Inc., Preoria

    Google Scholar 

  • Church HK (1981) Excavation handbook. McGraw-Hill Inc., New York

    Google Scholar 

  • Faradonbeh RS, Monjezi M, Armaghani DJ (2015) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput. doi:10.1007/s00366-015-0404-3

    Google Scholar 

  • Faradonbeh RS, Armaghani DJ, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75(3):993–1006

    Article  Google Scholar 

  • Fookes PG, Hawkins AB (1988) Limestone weathering: its engineering significance and a proposed classification scheme. Q J Eng Geol Hydrogeol 21(1):7–31

    Article  Google Scholar 

  • Fowell RJ (1993) The mechanics of rock cutting, vol 4. Pergamon & Press, Oxford, pp 155–189

    Google Scholar 

  • Fowell RJ, Johnson ST (1991) Cuttability assessment applied to drag tool tunnelling machines. In: Wittke AA (ed) Proceedings of 7th international congress rock mechanics. ISRM, Balkema, Achen, pp 985–990

  • Franklin JA, Broch E, Walton G (1971) Logging the mechanical character of rock. Trans Inst Min Metall 80:A1–A9

    Google Scholar 

  • Hadjigeorgiou J, Poulin R (1998) Assessment of ease of excavation of surface mines. J Terramech 35:137–153

    Article  Google Scholar 

  • Hardy MP, Goodrich RR (1992) Solution mining cavity stability: a site investigation and analytical assessment In: ISRM symposium Eurock’92. Rock characterization, British Geotechnical Society, London, pp 293–297

  • Hudson JA (1999) Technical auditing of rock mechanics modeling and rock engineering design. In: Rock Mechanics for Industry, pp 183–197

  • ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods. International Society for Rock Mechanics

  • Jahed Armaghani D, Amin MFM, Yagiz S, Faradonbeh RS, Abdullah RA (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186

    Google Scholar 

  • Karpuz C (1990) A classification system for excavation of surface coal measures. Min Sci Technol 11:157–163

    Article  Google Scholar 

  • Khandelwal M, Jahed Armaghani D (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34(2):605–620

    Article  Google Scholar 

  • Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396

    Article  Google Scholar 

  • Kirsten HAD (1982) A classification system for excavation in natural materials. Die Siviele Ingenieur in Suid Afrika, South Afrika, pp 293–308

    Google Scholar 

  • Komoo I (1995) Geologi kejuruteraan—perspektif rantau tropika lembap. Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia

    Google Scholar 

  • Kramadibrata S (1998) Assessment on the performance of pontinous surface miners. In: Proceedings of the 11th international symposium on mine planning and equipment selection, Canada, 551–556

  • MacGregor F, Mostyn GR, Jafari MR (1993) Mechanism of failure of artificial jointed rock due to ripper penetration. In: Proceeding of the international symposium on assessment and prevention of failure phenomena in rock engineering, Balkema Publisher, Istanbul, Turkey, pp 417–423

  • Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213

    Article  Google Scholar 

  • McLean AC, Gribble CD (1985) Geology for civil engineers, 2nd edn. George Allen and Unwin, Australia, p 314

    Google Scholar 

  • Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7(2):505–511

    Article  Google Scholar 

  • Monjezi M, Baghestani M, Faradonbeh RS, Saghand MP, Armaghani DJ (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Eng Comput 32(4):717–728

    Article  Google Scholar 

  • Muftuoglu YV (1983) A study of factors affecting diggability in British surface coal mines. Ph.D. thesis, University of Nottingham, Nottingham (unpublished)

  • Pettifer GS, Fookes PG (1994) A revision of the graphical method for assessing the excavability of rock. Q J Eng Geol 27:145–164

    Article  Google Scholar 

  • Priest SD, Hudson JA (1976) Discontinuity spacings in rock. Int J Rock Mech Min Sci Geomech Abstr 13:135–148

    Article  Google Scholar 

  • Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC (2013) Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Hazards 65(1):315–330

    Article  Google Scholar 

  • Scoble MJ, Muftuoglu YV (1984) Derivation of a diggability index for surface mine equipment selection. Min Sci Technol 1:305–322

    Article  Google Scholar 

  • Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci. doi:10.1007/s12517-015-1952-y

    Google Scholar 

  • Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23(3):249–262

    Article  Google Scholar 

  • Singh RN, Elmherig AM, Sunu MZ (1986) Application of rock mass characterization to the stability assessment and blast design in hard rock surface mining excavations. In: Proceedings of the 27th US symposium on rock mechanics, Alabama, pp 471–478

  • Singh RN, Denby B, Egretli I (1987) Development of a new rippability index for Coal Measures excavations. In: Proceedings of the 28th U.S. symposium on rock mechanics, Tucson, AZ, Balkema, Boston, pp 935–943

  • Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res 63(1):32–38

    Google Scholar 

  • Singh TN, Verma AK, Singh V, Sahu A (2005) Slake durability study of shaly rock and its predictions. Environ Geol 47(2):246–253

    Article  Google Scholar 

  • Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45

    Article  Google Scholar 

  • Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119

    Article  Google Scholar 

  • Smith HJ (1986) Estimating rippability of rock mass classification. In: Proceedings of the 27th US symposium on rock mechanics, University of Alabama, USA, pp 443–448

  • SPSS Inc. (2007). SPSS for Windows (Version 16.0). SPSS Inc., Chicago

  • Thuro K, Plinninger RJ (2003) Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability. ISRM Technology Roadmap for Rock Mechanics, South African Institute of Mining and Metallurgy, pp 1–7

  • Thuro K, Plinninger RJ, Spaun G (2002) Drilling, blasting and cutting—is it possible to quantify geological parameters relating to excavatability?. In: Proceedings of the 9th congress of the international association for engineering geology and the environment. Durban, South Africa: Engineering Geology for Developing Countries, pp 2853–2862

  • Tonnizam Mohamad E, Abad SVANK, Saad R (2011) Challenges of excavation by ripping works in weathered sedimentary zone. Electron J Geotech Eng 16:1337–1350

    Google Scholar 

  • Tonnizam Mohamad E, Noorani SA, Armaghani DJ, Saad R (2012) Simulation of blasting induced ground vibration by using artificial neural network. Electron J Geotech Eng 17:2571–2584

    Google Scholar 

  • Tonnizam Mohamad E, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl. doi:10.1007/s00521-016-2359-8

    Google Scholar 

  • Tsiambaos G, Saroglou H (2010) Excavatability assessment of rock masses using the geological strength index (GSI). Bull Eng Geol Environ 69(1):13–27

    Article  Google Scholar 

  • Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233

    Article  Google Scholar 

  • Weaver JM (1975) Geological factors significant in the assessment of rippability. Civil Eng South Afr 12:313–316

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre of Tropical Geoengineering (GEOTROPIK), Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Malaysia

    Edy Tonnizam Mohamad

  2. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Danial Jahed Armaghani

  3. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Mahyar Ghoroqi

  4. Young Researchers and Elites club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Behnam Yazdani Bejarbaneh

  5. Multimedia Information Technology Engineering, Shahid Beheshti University, Tehran, Iran

    Tadeh Ghahremanians

  6. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Muhd Zaimi Abd Majid

  7. Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Omid Tabrizi

Authors
  1. Edy Tonnizam Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahyar Ghoroqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behnam Yazdani Bejarbaneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tadeh Ghahremanians
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhd Zaimi Abd Majid
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Omid Tabrizi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danial Jahed Armaghani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tonnizam Mohamad, E., Jahed Armaghani, D., Ghoroqi, M. et al. Ripping Production Prediction in Different Weathering Zones According to Field Data. Geotech Geol Eng 35, 2381–2399 (2017). https://doi.org/10.1007/s10706-017-0254-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-017-0254-4

Keywords

Search

Navigation