Affiliation:
1. Galgotias University, India
2. VIT University, India
3. National Institute of Technology Patna, India
4. University of Massachusetts – Lowell, USA
Abstract
Elastic Modulus (Ej) of jointed rock mass is a key parameter for deformation analysis of rock mass. This chapter adopts three intelligent models {Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN)} for determination of Ej of jointed rock mass. MPMR is derived in a probability framework. ELM is the modified version of Single Hidden Layer Feed forward network. GRNN approximates any arbitrary function between the input and output variables. Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (s3) (MPa), and elastic modulus (Ei) (GPa) of intact rock have been taken as inputs of the ELM, GRNN and MPMR models. The output of ELM, GRNN and MPMR is Ej of jointed rock mass. In this study, ELM, GRNN and MPMR have been used as regression techniques. The developed GRNN, ELM and MPMR have been compared with the Artificial Neural Network (ANN) models.
Reference55 articles.
1. Prediction of gaseous emissions from industrial stacks using an artificial intelligence method
2. Arora, V. K. (1987). Strength and deformation behavior of jointed rocks (Ph.D. Thesis). Indian Institute of Technology, Delhi, India.
3. Engineering classification of rock masses for the design of tunnel support
4. Bieniawski, Z. T. (1973). Engineering classification of jointed rock masses. Transaction of South African Institution of Civil Engineers, 15(12), 335-344.
5. Determining rock mass deformability: experience from case histories
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献