Robust closed conduit structure stress anomaly identification method and system

Abstract

The invention belongs to the technical field of hydraulic engineering safety monitoring, and particularly relates to a robust closed conduit structure stress anomaly identification method and system.The closed conduit monitoring section historical data is collected as sample data, and the closed conduit monitoring section data is composed of environment measurement point data and steel bar stress measurement point data changing along with the environment quantity; a machine learning modeling strategy of a two-stage gradient lifting regression tree is adopted to construct each reinforcing steel bar stress measuring point prediction model, and historical monitoring data is utilized to perform training optimization; collecting monitoring section data of the closed conduit at regular time, sending the section data into the trained and optimized steel bar stress prediction model for prediction, and comparing a predicted numerical value with actually measured steel bar stress data in the monitoring data to judge whether a measuring point is abnormal or not. According to the method, when the closed conduit structure stress anomaly identification model is constructed, environment measurement point information is utilized, steel bar stress measurement point information at other positions is indirectly utilized, the problem that the constructed model possibly has endogenesis is avoided, closed conduit structure stress anomaly can be accurately identified, and the safety and reliability of the closed conduit structure can be evaluated in time.

Description

technical field [0001] The invention belongs to the technical field of water conservancy engineering safety monitoring, and in particular relates to a method and system for robust stress abnormality identification of culvert structures. Background technique [0002] The culvert is a common water conveyance structure. The safety of the culvert structure is the basic condition to ensure the normal operation of the project. If the structural instability occurs, it will lead to a series of environmental and social disasters. Through the data analysis of engineering safety monitoring equipment (mainly steel stress gauges), the safety behavior of the structure can be evaluated to detect engineering structural problems as early as possible. As early as the 1970s, some scholars have used regression modeling and analysis of safety monitoring data to realize the prediction of structural effect quantities (such as displacement, stress-strain, and osmotic pressure) and abnormal identifi...

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Problems solved by technology

On the contrary, the two have their own advantages and disadvantages, which are shown in: the statistical modeling method has a relatively solid theoretical foundation and physical explanation, and is widely used in engineering practice, but the model is also affected by problems such as multicollinearity when it is constructed and used. Due to constraints, the prediction accuracy rate is not high enough; machine learning modeling methods have the characteristics of self-organization, self-adaptation and nonlinear mapping, and the prediction accuracy rate is relatively high under laboratory conditions. However, since most machine learning models are black-box models, the model The training process is difficult to control, and overfitting is easy to occur, resulting in poor model stability and generalization ability, which affects the actual application effect in safety monitoring analysis

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