Prediction of geomechanical parameters using soft computing and multiple regression approach

[Display omitted] •Prediction of mechanical properties from density, porosity and P-wave velocity of the rock.•Comparative study of MVRA vs ANFIS.•Easy adoptable and useful MVRA model.•Statistical predictive model for Basaltic rock.•Application of soft computing for prediction of geomechanical prope...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2017-03, Vol.99, p.108-119
Main Authors: Singh, Rajesh, Umrao, Ravi Kumar, Ahmad, M., Ansari, M.K., Sharma, L.K., Singh, T.N.
Format: Article
Language:English
Subjects:
Adaptive neuro-fuzzy inference system
Adaptive systems
Artificial neural networks
Compressive strength
Correlation coefficients
Empirical analysis
Fuzzy logic
Fuzzy sets
Fuzzy systems
Geomechanics
Geotechnical engineering
Geotechnology
Modulus of elasticity
Multiple variable regression analysis
Parameter estimation
Parameters
Physical properties
Porosity
Predictions
Regression analysis
Root-mean-square errors
Soft computing
Statistical analysis
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Summary:[Display omitted] •Prediction of mechanical properties from density, porosity and P-wave velocity of the rock.•Comparative study of MVRA vs ANFIS.•Easy adoptable and useful MVRA model.•Statistical predictive model for Basaltic rock.•Application of soft computing for prediction of geomechanical properties. The evaluation of geotechnical parameters of geo-materials are essential part of every geotechnical project. But sometimes, it is not possible to determine the all required parameter in the laboratory. Therefore, scientist and engineers used the statistical and empirical relation to determine the crucial parameters. The present study focused on the determination of parameters like uniaxial compressive strength (UCS), tensile strength (TS), point load index (PLI) and Young’s modulus (YM) from very easily determinable physical parameters viz. density (DEN), porosity (PORO) and compressional wave velocity (P-WV) using multiple variable regression analysis (MVRA) and adaptive neuro-fuzzy inference system (ANFIS). The various ANFIS structures and MVRA models were tried for prediction of desired parameters and best one was considered based on variance account for (VAF), root mean square error (RMSE) and correlation coefficient (R2). ANFIS structure not only depends on the input parameters and rules, but also on the output parameter as observed in case of PLI.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2016.12.023