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

A health status and neural network technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as strong subjectivity, increased difficulty, difficult calculation, etc., achieve high objectivity and scientificity, and realize automation , the effect of improving the evaluation efficiency

Inactive Publication Date: 2018-06-08
GUANGZHOU INSTITUTE OF BUILDING SCIENCE CO LTD +1
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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.

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

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Embodiment Construction

[0045] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0046] The present invention provides a kind of assessment method of bridge health status based on radial basis function neural network, comprising the following steps:

[0047] Step S1: Obtain a data value group of network evaluation parameters and physical quantities of the bridge;

[0048] Step S2: According to the network evaluation parameters and the data value, calculate the evaluation value of the health status of the bridge through the radial basis function neural network;

[0049] Step S3: Comparing the evaluation value with the bridge damage index level, judging the health status level of the bridge.

[0050] Among them, the network evaluation parameters ...

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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...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/13
Inventor 季璇苏键谭灵生黎杰明黄晓丹
Owner GUANGZHOU INSTITUTE OF BUILDING SCIENCE CO LTD
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