A new method for classifying rock mass quality based on MCS and TOPSIS

Rock mass quality classification is essential in rock engineering. In practical engineering the parameters of rock mass vary with sampling disturbance and testing instruments, however obey a certain distribution. In other words, the classification of rock mass quality includes an uncertainty caused by the randomness of the parameters of the rock mass in geological formations. Traditional rock mass classification methods ignore the effect of this parameter uncertainty. In this paper, we propose a new method for evaluating rock mass quality considering the effect of parameter uncertainty through a rigorous reliability analysis. The weights of the classification system indexes are obtained using the game theory, combined with the technique for order preference by similarity to ideal solution (TOPSIS) in determining the limit-state function for reliability analysis. Stochastic uncertainty analysis is performed based on Monte Carlo simulation (MCS) and the limit-state function established by TOPSIS. The rock mass quality classification grade is obtained based on the probability calculation. The TOPSIS model with accurate game theory weighting is evaluated using 25 sets of samples. The results confirmed the reliability of the model. In a case study of rock mass surrounding a cavern, we verified the proposed rock quality classification method using certainty and uncertainty methods in MATLAB. The results demonstrate that the MCS–TOPSIS coupled model is efficient and accurate for classifying rock mass quality, and this approach is easy to implement.

[1]  L. Wu,et al.  Theoretical analysis and model test for rainfall-induced shallow landslides in the red-bed area of Sichuan , 2018, Bulletin of Engineering Geology and the Environment.

[2]  Z. Bieniawski.  Determining rock mass deformability: experience from case histories , 1978 .

[3]  Sankaran Mahadevan,et al.  Reliability analysis under epistemic uncertainty , 2016, Reliab. Eng. Syst. Saf..

[4]  Serhat Yilmaz,et al.  An assessment of total RMR classification system using unified simulation model based on artificial neural networks , 2011, Neural Computing and Applications.

[5]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[6]  Ya Ching Liu,et al.  A new approach for application of rock mass classification on rock slope stability assessment , 2007 .

[7]  Kok-Kwang Phoon,et al.  Impact of Statistical Uncertainty on Geotechnical Reliability Estimation , 2016 .

[8]  Hossein Nezamabadi-pour,et al.  Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system , 2011 .

[9]  Yu Wang,et al.  Bayesian characterization of correlation between uniaxial compressive strength and Young's modulus of rock , 2016 .

[10]  Yu Wang,et al.  Evaluating Variability and Uncertainty of Geological Strength Index at a Specific Site , 2016, Rock Mechanics and Rock Engineering.

[11]  Z. Fuat Toprak,et al.  Fuzzy-based quality classification model for natural building stone blocks , 2012 .

[12]  Hossein Jalalifar,et al.  Prediction of rock mass rating system based on continuous functions using Chaos–ANFIS model , 2015 .

[13]  Ewa Roszkowska,et al.  The fuzzy saw and fuzzy TOPSIS procedures based on ordered fuzzy numbers , 2016, Inf. Sci..

[14]  E. Hoek,et al.  Rock slope engineering , 1974 .

[15]  Yu Wang,et al.  Bayesian perspective on geotechnical variability and site characterization , 2016 .

[16]  E. Akyol,et al.  Geotechnical land suitability assessment using spatial multi-criteria decision analysis , 2016, Arabian Journal of Geosciences.

[17]  Vamegh Rasouli,et al.  Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms , 2012, Rock Mechanics and Rock Engineering.

[18]  L. Wu,et al.  Overhanging Rock: Theoretical, Physical and Numerical Modeling , 2018, Rock Mechanics and Rock Engineering.

[19]  Kamran Goshtasbi,et al.  A multi-dimensional approach to the assessment of tunnel excavation methods , 2011 .

[20]  Fang Liu,et al.  Fire risk assessment for large-scale commercial buildings based on structure entropy weight method , 2017 .

[21]  Qiang Huang,et al.  Integrated index for drought assessment based on variable fuzzy set theory: A case study in the Yellow River basin, China , 2015 .

[22]  Jun Zhuang,et al.  A review of game theory applications in natural disaster management research , 2017, Natural Hazards.

[23]  Xindong Peng,et al.  A modified TOPSIS method based on vague parameterized vague soft sets and its application to supplier selection problems , 2019, Neural Computing and Applications.

[24]  L. Zhang,et al.  Evolution of lakes created by landslide dams and the role of dam erosion: A case study of the Jiajun landslide on the Dadu River, China , 2019, Quaternary International.

[25]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[26]  Arun Kumar Sangaiah,et al.  An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome , 2015, Neural Computing and Applications.

[27]  Wei Zhang,et al.  Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method , 2008 .

[28]  Wengang Dang,et al.  Rock quality classification and stability evaluation of undersea deposit based on M-IRMR , 2014 .

[29]  Alberto Refice,et al.  Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment , 2002 .

[30]  Da Huang,et al.  Direct Shear Tests of Sandstone Under Constant Normal Tensile Stress Condition Using a Simple Auxiliary Device , 2017, Rock Mechanics and Rock Engineering.

[31]  Xiaoyu Chen,et al.  A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory , 2015, Natural Hazards.

[32]  Abdul Ghafoor,et al.  Weighted fusion of MRI and PET images based on fractal dimension , 2017, Multidimens. Syst. Signal Process..

[33]  A. J. Hendron,et al.  Design Of Surface And Near-Surface Construction In Rock , 1966 .

[34]  Vipul Jain,et al.  Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry , 2018, Neural Computing and Applications.

[35]  Jun Yu Li,et al.  A Novel Model of Set Pair Analysis Coupled with Extenics for Evaluation of Surrounding Rock Stability , 2015 .

[36]  Haluk Akgün,et al.  Landfill site selection utilizing TOPSIS methodology and clay liner geotechnical characterization: a case study for Ankara, Turkey , 2014, Bulletin of Engineering Geology and the Environment.

[37]  V. Doyuran,et al.  Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey , 2004 .

[38]  Yanli Wu,et al.  GIS based frequency ratio and index of entropy models to landslide susceptibility mapping (Daguan, China) , 2016, Environmental Earth Sciences.

[39]  Zhou Hui,et al.  APPLICATION OF EXTENICS THEORY TO EVALUATION OF UNDERGROUND CAVERN ROCK QUALITY BASED ON STRATIFICATION ANALYSIS METHOD , 2006 .

[40]  Nick Barton,et al.  Engineering classification of rock masses for the design of tunnel support , 1974 .

[41]  Y. Zhou,et al.  Laboratory characterization of rainfall-induced loess slope failure , 2017 .