Analysis of Ground Settlement of Foundation Pit Using Optimized LSTM Based on Improved Particle Swarm Optimization Algorithm
The long-term and short-term memory network(LSTM)has strong nonlinear fitting ability,which has been widely used in the prediction and analysis of foundation pit deformation or settlement.How-ever,there is a challenging issue of determining the important parameters in LSTM.In this study,an im-proved particle swarm optimization algorithm(IPSO)is proposed to optimize the number of iterations,batch size,and the number of hidden layer neurons in LSTM neural network.By introducing the mutation mechanism of genetic algorithm,IPSO avoids the local optimization of PSO in the early stage of the optimi-zation,and improves the optimization efficiency of PSO by using the method of nonlinear weight and impro-ving learning factor.The IPSO-LSTM is used to predict and analyze the actual foundation pit settlement,and a comparison with PSO-LSTM and LSTM is presented.It is shown that the average percentage error of IPSO-LSTM is 56.47％and 11.92％lower than that of LSTM and PSO-LSTM respectively,demonstrating great potential of IPSO-LSTM in accurately predicting the surface settlement of foundation pit.
万方数据
维普
