Abstract

Abstract In order to study the waterproof performance of elastic rubber gasket in shield tunnel lining joints, an innovative sensitivity analysis method is proposed by combining the Monte Carlo method with the stochastic finite element method (FEM) in this paper. The sensitivity values of the waterproof performance respecting to elastic rubber gaskets are obtained via the ANSYS Probabilistic Design System (PDS) module, in which the parameters of material hardness, coordinates of the hole center, apertures are selected as random input variables. Meantime, the extent of the tolerance effect of the random parameters on the waterproof performance is explored.

Highlights

  • Waterproofing is one of the cruxes for underground projections, which should be paid especial attention to in shield tunnels

  • Since the geometry sizes, machining errors, materials, actual working loads are all random for the same batch of products in practical engineering, we need to use stochastic finite element method (FEM) to carry out the detailed analysis

  • The stochastic FEM [9] used in this paper is a combination of the Monte Carlo method and the FEM

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Summary

Introduction

Waterproofing is one of the cruxes for underground projections, which should be paid especial attention to in shield tunnels. In addition to the impermeability of waterproof materials, the optimization design method of rubber gaskets has attracted considerable attention. The effects of the uncertainties of geometry sizes, machining errors and material parameters on the waterproof performance of rubber gaskets are not considered in previous optimization processes. We close the paper with some conclusions and remarks

Stochastic FEM based on the Monte Carlo method and ANSYS PDS module
The principle of Monte Carlo stochastic method
Sampling methods for random variables
Constructing parameterized model
Statistical characteristics of random parameters
Finite element model
Sensitivity analysis based on Spearman rank correlation coefficient
Sensitivity calculation results
Sensitivity influence of tolerance
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