Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression

Abstract The mining industry relies heavily on the use of empirical methods and charts for the design and assessment of entry-type excavations. The commonly adopted empirical design method, commonly referred to as the critical span graph, which was specifically developed for the assessment of rock stability in entry-type excavations, was based on an extensive database of cut and fill mining operations and case histories in Canada. It plots the critical span versus the rock mass rating for the observed case histories and has been widely accepted for an initial span design of cut and fill stopes. Different approaches, either based on classical regression and classification statistical techniques or even the supervised machine learning methods, have been proposed to classify the observed cases into stable, potentially unstable and unstable groups. This paper presents a new assessment approach which combines the use of a multivariate adaptive regression splines (MARS) approach and the logistic regression (LR) method. The proposed MARS_LR model can capture and describe the intrinsic, complex relationship between input descriptors and the dependent response without having to make any assumptions about the underlying relationship. Considering its simplicity in interpretation, predictive accuracy, its data-driven and adaptive nature plus the ability to map the interaction between variables, the use of MARS_LR model in evaluating stability of underground entry-type excavations is promising.

[1]  Nii O. Attoh-Okine,et al.  Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling , 2009 .

[2]  Richard J. Bathurst,et al.  Comparison of numerical and analytical solutions for reinforced soil wall shaking table tests , 2011 .

[3]  Yang Xiao,et al.  Determination of earth pressure balance tunnel-related maximum surface settlement: a multivariate adaptive regression splines approach , 2018, Bulletin of Engineering Geology and the Environment.

[4]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[5]  Wengang Zhang,et al.  Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression , 2016 .

[6]  Pijush Samui,et al.  Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach , 2012 .

[7]  J. Friedman Multivariate adaptive regression splines , 1990 .

[8]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines for analysis of geotechnical engineering systems , 2013 .

[9]  Jianye Ching,et al.  Simplified procedure for estimation of liquefaction-induced settlement and site-specific probabilistic settlement exceedance curve using cone penetration test (CPT) , 2013 .

[10]  Pijush Samui,et al.  Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay , 2012, Int. J. Appl. Metaheuristic Comput..

[11]  Brennan Lang Span design for entry-type excavations , 1994 .

[12]  Wengang Zhang,et al.  Multivariate adaptive regression splines for inverse analysis of soil and wall properties in braced excavation , 2017 .

[13]  Anthony T. C. Goh,et al.  An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines , 2014 .

[14]  J. Wang,et al.  Application of a neural network in the empirical design of underground excavation spans , 2002 .

[15]  C. Hsein Juang,et al.  Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data , 2012 .

[16]  P. J. García Nieto,et al.  Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers , 2016, Materials.

[17]  Amir Hossein Gandomi,et al.  Permanent deformation analysis of asphalt mixtures using soft computing techniques , 2011, Expert Syst. Appl..