基于有限元与LSTM机器学习模型的边坡稳定性预警分析OACSTPCD

Based on the relationship between precipitation and water content of the slope,the correlation between physical and mechanical properties of the slope and water content was explored.Then,the big data analysis of historical precipitation in a certain area was carried out,and the long short-term memory(LSTM)model was used to predict future precipitation.After that,the finite element strength reduction method was used to analyze and simulate the slope failure and displacement by FLAC3D,and the slope stability coefficient was calculated.The water content of the slope corresponding to the critical stability coefficient was found,and the corresponding precipitation was obtained.According to the prediction,the possible dangerous period was clarified for early warning,so as to facilitate the protection and management of engineering personnel.The results find that the dangerous situation is concentrated in some periods from April to September,which provides a new feasible response method for slope early warning and treatment in the future.