Evaluation of soft soil foundation reinforcement effect of prefabricated building based on BP neural network

Abstract

The use of traditional reinforcement methods in construction sites often causes problems such as pore water pressure, which can not effectively form a solid foundation. Aiming at this problem, the evaluation model of soft soil foundation reinforcement effect of prefabricated buildings is established based on BP neural network, combined with the geological characteristics of soft soil and the elements of foundation reinforcement; The L-M algorithm is used to optimize the slow convergence problem of BP neural network, and finally its evaluation effect is verified through practical application. The results show that the strengthening effect of 1550 kN $\cdot$ m/m ${}^2$ is better than that of 2000 kN $\cdot$ m/m ${}^2$ with the more times of tamping for marine and river facies, and there is a positive correlation between the times of strengthening and the effect. At the same time, similar qualitative conditions also show that the greater the burial depth, the worse the reinforcement effect. When the overlying soil layer is soft, the shallow buried soil layer can be reinforced by laying a cushion to improve the overall reinforcement effect. The laws reflected in the final model output data are the same as those reflected in the construction, and the accuracy of the proposed model is up to 87%, indicating that the model has superior performance in the reinforcement effect evaluation.