Hybrid intelligent method optimization of a soft rock replacement scheme for a large cavern excavated in alternate hard and soft rock strata

Abstract

Optimization of a soft rock replacement scheme for a large cavern excavated in alternating hard and soft rock strata is a complicated non-linear mechanical problem having a large parameter search space. Obtaining a global optimum solution is the key to the problem. A hybrid intelligent method is proposed for this purpose. It is an integration of an evolutionary neural network and finite element analysis using a genetic algorithm. The non-linear relation of the soft rock replacement scheme with the displacement and damage zone of the cavern due to excavation in the given geological conditions is learnt and represented by a forward neural network whose structure and connection weights are global optimally recognized by using the genetic algorithm. The learning samples are obtained from finite element calculations. The optimal soft rock replacement scheme, having the minimal displacement and damage volume induced by cavern excavation, is searched in a global space using the genetic algorithm. The new methodology is used to evaluate soft rock replacement schemes for the Shuibuya cavern in China excavated in strata consisting of alternating soft and hard rocks. The results indicate that the new methodology can recognize the optimal soft rock replacement scheme for a large cavern in such complicated geological conditions and the neural network model can provide a solution which is close to the finite element analysis for the same geological and construction conditions.