Method for dynamically recognizing overweight vehicle in bridge monitoring system

Abstract

The invention discloses a method for dynamically recognizing an overweight vehicle in a bridge monitoring system. The method comprises the steps of 1) ensuring a strain response time sequence to be astationary sequence and if not, transforming time sequence data into a stationary time sequence through step difference and order difference; 2) modeling the time sequence data using an SARIMA model,taking an AR coefficient of the model as a key feature to identify an abnormality, and then connecting the coefficients of different sensors in the same section in series to obtain a feature vector; 3) inputting the AR coefficient feature vector into a noise reduction automatic encoder for training, after the training is completed, obtaining a middle layer dimension in a network structure of the automatic encoder, that is, the required key feature, and taking the middle layer dimension as a final training feature; and 4) inputting the training feature into a one-class support vector machine with a kernel function for unsupervised training, with a training result being a hypersphere in a high-dimensional space, and then, using the hypersphere to determine whether the test data is overweightabnormal data. The method in the invention is simple and efficient in identification principle and has strong robustness.

Description

technical field [0001] The invention relates to the technical field of overweight vehicle identification on long-span bridges, in particular to a dynamic identification method for overweight vehicles in a bridge monitoring system. Background technique [0002] As an important transportation hub, bridges will be affected by external unfavorable loads such as earthquakes, ship collisions, strong winds, rain and snow, and overweight vehicles in the actual environment. Overweight vehicles often damage bridge structures due to their high frequency of occurrence, affecting traffic safety. The society has caused great economic losses and casualties. Therefore, the identification of overloaded vehicles, statistical analysis and traffic management of overloaded vehicles are of great significance for monitoring bridge health, evaluating bridge status, reducing safety accidents, prolonging bridge service life, and guiding vehicle traffic. [0003] The current static weighing mostly us...

AI Technical Summary

Problems solved by technology

The identification method based on the influence matrix needs to establish a finite element analysis model of the bridge, and calculate the influence of various characteristic loads on the sensor respectively. The method theory is relatively complicated, and the user is required to be familiar with bridge r

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