Bridge health condition evaluation method and system based on radial base function neural network

Abstract

The invention provides a bridge health condition evaluation method based on a radial base function neural network. The method includes the following steps that S1, network evaluation parameters and adata value set of physical quantities of a bridge are obtained; S2, according to the network evaluation parameters and the data value set, a bridge health condition evaluation value is calculated through the radial base function neural network; S3, the evaluation value and a bridge damage index grade are compared, so that the health condition grade of the bridge is judged. The evaluation process of the method has high objectivity and scientificity. Meanwhile, the invention further provides an evaluation system based on the method. The evaluation system is used for achieving automation of the evaluation process, manual calculation of an evaluator is not needed, and the bridge evaluation efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of bridge engineering health monitoring, and in particular relates to a method and system for evaluating the health status of bridges based on a radial basis function neural network. Background technique [0002] Bridge construction is one of the important infrastructures of the country, and bridge engineering is the lifeline project related to the coordinated development of society and economy. Due to environmental impacts, increasing traffic volumes, and the road transport industry often advocating higher vehicle load standards, many urban bridges fail to meet the structural requirements set forth for new bridge designs; degradation and increased loads also contribute to reduced bridge reliability , leaving potential safety hazards, causing irreparable economic losses and safety accidents. Therefore, it is of great theoretical and practical significance to establish a scientific bridge health assessment s...

AI Technical Summary

Problems solved by technology

Traditional bridge health evaluation methods generally use expert evaluation methods or fuzzy evaluation methods. These methods are highly subjective and difficult to calculate, making it difficult for evaluators to grasp the health status of bridges in real time.

Images