MultiCategory OrderedDependentVariable Logistic Regression Model for Rock Mass Classification

Abstract:

Multicategory ordereddependentvariable logistic regression model was introduced into the rock mass classification.Based on rock mass samples data,rock uniaxial compressive strength,rock acoustic wave velocity,intensity of jointing,joint roughness coefficient,weathering variation coefficient of joint surface and permeability coefficient were chosen as independent variables,rock mass level was considered as dependent variable,then rock mass classification judgment formula was established.Goodness of fit and model predictive ability test were carried out to evaluate the model correctness.The results show that Logistic regression analysis model has excellent performance,misjudging rate of training samples is zero,and predictive ability is strong.Compared to the distance discriminant analysis,and linear regression analysis, Logistic regression analysis has no normal distribution restriction to independent variables,and it is theologically appropriate to analyze rock mass classification problems whose dependent variable is discrete ordered variable,the output of this method is probability value of all levels that rock mass belong to,which provides additional rock mass information to engineering designer.Thus multicategory ordereddependentvariable regression is a superior method for rock mass classification.