Buried Pipeline Collapse Dynamic Evolution Processes and Their Settlement Prediction Based on PSO-LSTM

Abstract

Based on the unit life and death technology, the dynamic evolution process of soil loss is considered, and a pipe-soil nonlinear coupling model of buried pipelines passing through the collapse area is constructed. The analysis shows that after the third layer of soil is lost, the existence of the “pipe-soil separation” phenomenon can be confirmed, which then supplements the assumption that “pipe-soil is always in contact” in the elastic foundation beam theory. Calculation of settlement deformation of buried pipelines It needs to be divided into two stages: cooperative deformation and non-cooperative deformation. Taking the settlement prediction of buried pipelines as the goal, the particle swarm algorithm (PSO) was used to optimize the number of neurons, Dropout, and Batch-size in the long short-term memory network (LSTM) structure. The optimization results were 60, 0.001, and 100, respectively. The PSO-LSTM model proposed in this article can accurately describe the dynamic evolution process of buried pipelines and has better prediction accuracy than the modified Gaussian curve method and LSTM neural network model. The use of this model can provide a reference for safety risk management, disaster early warning, and intelligent monitoring when buried pipelines suffer from soil collapse disasters.

Prediction of Buildings’ Settlements Induced by Deep Foundation Pit Construction Based on LSTM-RA-ANN

“…Experiments showed that the spatio-temporal relationship between the surface settlement data obtained from different monitoring points within the network is dynamic during metro pit excavation, and the STdeep model exhibits the best performance and excellent stability. Zhou et al [22] used the particle swarm algorithm (PSO) to optimise the number of neurons, dropout, and batch-size in the structure of LSTM network, and found that the PSO-LSTM model can accurately describe the dynamic evolution process of the buried pipelines, and has a better prediction accuracy than the improved Gaussian curve method and the LSTM neural network model.…”