Research On Stress And Deformation Monitoring Analysis And Operational Performance Evaluation Of Large Aqueduct

The aqueduct is a critical long-distance water conveyance structure in the Southto-North Water Diversion Project,and its safe operation is related to the stability of the entire trans-basin water conveyance system.The establishment of a safety analysis model for the aqueduct and the in-depth exploration of the effective information in the aqueduct monitoring data can effectively grasp the operation of the aqueduct,which is of great significance for ensuring the safety of the aqueduct and its structural stability.This paper mainly focuses on the safety monitoring and behavior evaluation of aqueducts.It studies the use of machine learning algorithms and statistical models of monitoring effects to predict the stress and strain of the aqueduct and the degree of joint opening and closing,and combines the results of the aqueduct prediction model analysis.The data fusion method analyzes the working behavior of the aqueduct and has been applied in actual engineering.The main research contents of this paper are as follows:(1)Aiming at the monitoring effect of the stress,strain and joint opening and closing degree of the aqueduct,the nuclear extreme learning machine algorithm is combined with the research results in the field of hydraulic building safety monitoring,and the optimization and nuclear limit based on the improved cuckoo search algorithm are established.Learning machine’s aqueduct safety monitoring model.This model takes water level,temperature,and time-dependent components as inputs,and achieves accurate prediction of various monitoring effects of the aqueduct.Comparison with other models verifies that the model has certain advantages,and the model has certain advantages.Good robustness and generalization ability provide reliable data support for the comprehensive safety evaluation of the aqueduct.(2)Aiming at the performance evaluation of the aqueduct,based on the results of modeling and analysis of the aqueduct monitoring data,the cloud model and data fusion methods commonly used in the study of uncertainty problems are used for further processing,and a cloud-based model theory and multiple measurement points are proposed.A comprehensive evaluation method for aqueduct safety based on the fusion of predictive analysis results.This method relies on the analysis results of the aqueduct safety predictive model to evaluate the operation of the aqueduct,effectively avoiding the subjectivity of the evaluation process.(3)Taking the aqueduct of a tributary of Shuangji River in the middle route of the South-to-North Water Transfer Project as the research object,the prediction accuracy and performance of the single-point safety monitoring model of the aqueduct based on the nuclear extreme learning machine were verified in the monitoring data of strain gauges,steel bar stress gauges and joint gauges.And further realized the overall performance evaluation of the Shuangji River branch aqueduct through the comprehensive evaluation method of aqueduct safety based on the integration of cloud model theory and multi-point prediction analysis results.The experimental results show that the method can accurately evaluate the working behavior of the aqueduct based on the analysis results of the predictive model,and provides a new method for the comprehensive safety evaluation of the aqueduct.