Abstract
This paper introduces a new analysis method – stochastic medium technique (SMT) combined with genetic programming (GP) in the prediction of ground subsidence due to tunneling in mountainous areas. The methodology involves the use of stochastic medium theory to generate theory models and to predict ground subsidence due to tunneling in mountainous areas. The parameters in the theory models which are optimized by genetic programming. The use of the integrated methodology is demonstrated via a case study in the prediction of ground subsidence due to tunneling in mountainous areas in Hebei, North China. The results show that the integrated stochastic medium technique – genetic programming (SMT-GP) gives the smallest error on the ground subsidence data when compared to traditional finite element method. The SMT-GP method is expected to provide a significant improvement when the ground subsidence data come from mountainous areas. The agreement of the theoretical results with the field measurements shows that the SMT-GP is satisfactory and the models and SMT-GP method proposed are valid and thus can be effectively used for predicting the ground surface subsidence due to tunneling engineering in mountainous areas and urban areas.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.