We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel.
- Authors
Xu, Jiancong; Sun, Chen; Rui, Guorong
- Abstract
How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels.
- Subjects
STOCHASTIC analysis; TUNNELS; LATIN hypercube sampling; MONTE Carlo method; TUNNEL ventilation; BACK propagation; GENETIC algorithms
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 5, p2127
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14052127