- Title
- An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass
- Creator
- Parsajoo, Maryama; Mohammed, Ahmed; Yagiz, Saffet; Armaghani, Danial; Khandelwal, Manoj
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188243
- Identifier
- vital:17238
- Identifier
-
https://doi.org/10.1016/j.jrmge.2021.05.010
- Identifier
- ISSN:1674-7755 (ISSN)
- Abstract
- 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
- Publisher
- Chinese Academy of Sciences
- Relation
- Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Rights
- Copyright © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Rights
- Open Access
- Subject
- 4005 Civil engineering; 4019 Resources engineering and extractive metallurgy; Artificial bee colony (ABC); Evolutionary computation; Field penetration index (FPI); Neuro-fuzzy technique; Tunnel boring machine (TBM)
- Full Text
- Reviewed
- Funder
- This study was supported by the Faculty Development Competitive Research Grant program of Nazarbayev University (Grant No. 021220FD5151 ).
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