Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. (January 2017)

• Record Type: Journal Article
• Authors: Khandelwal, Manoj; Shirani Faradonbeh, Roohollah; Monjezi, Masoud; Armaghani, Danial; Majid, Muhd; Yagiz, Saffet
• Is Part Of: Engineering with computers. Volume 33:Number 1(2017)
• Page Start: 13
• Page End: 21
• Publication Date: 2017-01
• Subjects: Brittleness -- Genetic programming -- Non-linear multiple regression; Engineering design -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Conception technique -- Informatique -- Périodiques; Conception assistée par ordinateur -- Périodiques; Electronic journals; 620.00285
• DOI: 10.1007/s00366-016-0452-3
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract Brittleness of rock is one of the most critical features for design of underground excavation project. Therefore, proper assessing of rock brittleness can be very useful for designers and evaluators of geotechnical applications. In this study, feasibility of genetic programming (GP) model and non-linear multiple regression (NLMR) in predicting brittleness of intact rocks is examined. For this purpose, a dataset developed by conducting various rock tests including uniaxial compressive strength, Brazilian tensile strength, unit weight and brittleness via punch penetration on rock samples gathered from 48 tunnels projects around the world is utilized herein. Considering multiple inputs, several GP models were constructed to estimate brittleness index of the rock and finally, the best GP model was selected. Note that, GP can make an equation for predicting output of the system using model inputs. To show applicability of the developed GP model, non-linear multiple regression (NLMR) was also applied and developed. Considering some model performance indices, performance prediction of the GP and NLMR models were evaluated and it was found that the GP model is superior to NLMR one. Based on coefficient of determination (R 2 ) of testing datasets, by proposing GP model, it can be improved from 0.882 (obtained by NLMR model) to 0.904. It is worth mentioning that the proposed predictive models in this study should be planned and used for the similar types of rock and the established inputs ranges.