We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Stability Evaluations of Unlined Horseshoe Tunnels Based on Extreme Learning Neural Network.
- Authors
Jearsiripongkul, Thira; Keawsawasvong, Suraparb; Banyong, Rungkhun; Seehavong, Sorawit; Sangjinda, Kongtawan; Thongchom, Chanachai; Chavda, Jitesh T.; Ngamkhanong, Chayut
- Abstract
This paper presents an Artificial Neural Network (ANN)-based approach for predicting tunnel stability that is both dependable and accurate. Numerical solutions to the instability of unlined horseshoe tunnels in cohesive-frictional soils are established, primarily by employing numerical upper bound (UB) and lower bound (LB) finite element limit analysis (FELA). The training dataset for an ANN model is made up of these numerical solutions. Four dimensionless parameters are required in the parametric analyses, namely the dimensionless overburden factor γD/c′, the cover-depth ratio C/D, the width-depth ratio B/D, and the soil friction angle ϕ. The influence of these dimensionless parameters on the stability factor is explored and illustrated in terms of a design chart. Moreover, the failure mechanisms of a shallow horseshoe tunnel in cohesive-frictional soil that is influenced by the four dimensionless parameters are also provided. Therefore, the current stability solution, based on FELA and ANN models, is presented in this paper, allowing for the efficient and accurate establishment and evaluation of an optimum surcharge loading of shallow horseshoe tunnels in practice.
- Subjects
TUNNELS; ARTIFICIAL neural networks; HORSESHOES; FINITE element method
- Publication
Computation, 2022, Vol 10, Issue 6, p81
- ISSN
2079-3197
- Publication type
Article
- DOI
10.3390/computation10060081