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An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

- Parsajoo, Maryama, Mohammed, Ahmed, Yagiz, Saffet, Armaghani, Danial, Khandelwal, Manoj

  • Authors: Parsajoo, Maryama , Mohammed, Ahmed , Yagiz, Saffet , Armaghani, Danial , Khandelwal, Manoj
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
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  • Description: Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

- Khandelwal, Manoj, Faradonbeh, Roohollah, Monjezi, Masoud, Armaghani, Danial, Bin Abd Majid, Muhd, Yagiz, Saffet

  • Authors: Khandelwal, Manoj , Faradonbeh, Roohollah , Monjezi, Masoud , Armaghani, Danial , Bin Abd Majid, Muhd , Yagiz, Saffet
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 33, no. 1 (2017), p. 13-21
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  • Description: 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.
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