Application of the artificial bee colony algorithm-based projection pursuit method in statistical rock mass stability es
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ORIGINAL ARTICLE
Application of the artificial bee colony algorithm-based projection pursuit method in statistical rock mass stability estimation Haojin Li • Junjie Li • Fei Kang
Received: 6 July 2011 / Accepted: 11 August 2012 / Published online: 25 August 2012 Springer-Verlag 2012
Abstract Because of the complexity of factors that affect rock mass stability, the design and decision-making in related engineering cannot rely solely on theoretical analysis and numerical calculation, but depend on the comprehensive judgment of experts. In pursuit of a statistical approach that may improve this disparity, an artificial bee colony algorithm-based projection pursuit (ABC-PP) method is presented for rock mass stability determination. The ABC-PP method is a powerful tool to deal with highdimension problems, which characterize rock mass stability assessment practice. Two experiments are employed to demonstrate the efficiency of the ABC-PP method. In the first case, the state of stability is classified at two levels: stable and failed, whereas in the second case stability is classified at five levels (1–5) to test the capability of multilevel prediction of the ABC-PP method. Results show that the ABC-PP method could predict the rock mass stability accurately and may also provide the relative importance of specific controls on stability. Keywords Rock mass stability estimation Projection pursuit method Artificial bee colony algorithm Multi-factor comprehensive evaluation
H. Li (&) J. Li F. Kang Department of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian City, Liaoning Province, China e-mail: [email protected]
Introduction The rock mass stability estimation is very important as its failure often leads to the loss of life and property. Until now, rock mass stability assessment methods could be classified as: empirical equation method, numerical calculation method, analytical calculation method and comprehensive judgment method. Due to the complexity and uncertainty of geomechanical factors affecting the stability, design and decision-making in related engineering cannot rely solely on theoretical analysis and numerical calculation. At present, most practical geotechnical engineering is designed by specialists’ comprehensive judgment based on the analogy of practical engineering (Liu and Chen 2007; Xu et al. 2008; Gong 2011). Therefore, how to use the data from practical engineering scientifically becomes the crucial issue to provide correct information to designers. Actually, the rock mass stability estimation is the complex nonlinear function approximation between the affecting factors and the stability state. During recent years, modern methods, such as artificial neural network (ANN) and support vector machine (SVM), have been applied in slope stability estimation (Chen et al. 2001; Ferentinou and Sakellariou 2007; Samui 2008). However, ANN has some limitations: it provides no information about the relative impo
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