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Application of soft computing and statistical methods to predict rock mass permeability

  • Application of soft computing
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Abstract

Permeability is one of the important issues that must be considered in the investigation of dam sites. Determination of this parameter in the boreholes is time-consuming, costly, and in some cases impossible. In this study, the values of Q classification system, Lugeon and joint spacing in five-meter intervals of boreholes in limestone rocks of Bazoft and Khersan II dam sites, in south Iran were determined. Then, the Lugeon number was estimated based on Q classification system and joint spacing in control and trial grouting boreholes by statistical analysis (SA), multilayer perceptron neural network (MPNN) by feed-forward method, support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF) methods. Results showed that rock mass is categorized in moderate permeability class based on mean Lugeon value and in the good category based on Q classification system. The Q classification system and joint spacing indicated the highest effect on the Lugeon and the depth showed the least effect on the Lugeon. The SA displayed that it is possible to predict Lugeon values by Q and Js with a precision higher than 78% based on the data of both dam sites. According to the criteria, the accuracy of the MPNN (R = 0.91), RF (R = 0.97), ANFIS (R = 0.93) and SA (R = 0.80–0.83) to estimate the Lugeon number was lower than SVR (R = 0.98) method.

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Acknowledgements

We sincerely thank the cooperation of Ghods Niroo and Mahab Ghods Consulting Engineering Companies in the realization of this research.

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Authors and Affiliations

  1. Petroleum Engineering Department, Australian University, West Mishref, Kuwait

    S. M. Alizadeh

  2. Engineering Faculty of Khoy, Urmia University of Technology, Urmia, Iran

    Amin Iraji

Authors
  1. S. M. Alizadeh
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  2. Amin Iraji
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S.M.A.: data analysis, analysis of results, compilation, writing, review and editing; A.I.: performing field investigations and collecting required data, rock mechanics advisor, review and editing the manuscript.

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Correspondence to Amin Iraji.

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Alizadeh, S.M., Iraji, A. Application of soft computing and statistical methods to predict rock mass permeability. Soft Comput 27, 5831–5853 (2023). https://doi.org/10.1007/s00500-022-07586-8

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