SMT-GP method of prediction for ground subsidence due to tunneling in mountainous areas

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.

► Stochastic medium combined with genetic programming in the subsidence prediction.
► The parameters in the theory models which are optimized by genetic programming.
► GP uses continuous functions when applied to the prediction of theory parameters.
► SMT-GP technique can improve upon more traditional analysis techniques.
► SMT-GP calculations are much closer to measured points than FEM predictions.