Normal average value based bridge damage recognition method

Abstract

The present invention discloses a normal average value based bridge damage recognition method. The specific recognition is partitioned into eight steps of: selecting a bridge to be monitored, performing structure analysis on the bridge, and determining a monitoring item; performing layout of monitoring points, wherein layout contents comprise monitoring point codes, corresponding sensor types, and spatial three-dimensional coordinates; according to a layout scheme of the monitoring points, acquiring a stress, deformation, displacement, cable tension and modal data of each monitoring point for more than one year; according to an average value mu and standard deviation sigma of a statistic result, establishing a normal average value model of monitoring data corresponding to each monitoring point; and after establishing the normal average value model of different monitoring data of each monitoring point, according to position information and the spatial three-dimensional coordinates of each monitoring point, finding a damaged structure and a damaged position so as to facilitate bridge maintenance management personnel to perform maintenance and repair work, thereby ensuring security operation of a bridge. The normal average value based bridge damage recognition method provided by the present invention provides a corresponding determination condition for a bridge management and maintenance unit on bridge damage diagnosis and maintenance management, and has importance significance for development of a bridge health monitoring system.

Description

technical field [0001] The invention relates to a bridge damage identification method, in particular to a bridge damage identification method based on a normal mean value, and belongs to the technical field of bridge health monitoring. Background technique [0002] At present, the main basis for judging the damage of bridge structures is bridge periodic inspection and bridge load test. Among them, the bridge periodic inspection mainly judges the disease of the bridge surface, while the bridge load test mainly judges the bridge's bearing capacity and structural stiffness. These two detection methods not only require a large investment of manpower and material resources, but also part of the detection needs to cut off the bridge traffic, which has a certain impact on the normal operation of the bridge. These two detection methods belong to the "hindsight" detection method, which cannot play an early warning role. Moreover, this manual detection method cannot achieve the purpo...

AI Technical Summary

Problems solved by technology

These two detection methods not only require a large investment of manpower and material resources, but also part of the detection needs to cut off the bridge traffic, which has a certain impact on the normal operation of the bridge.

These two detection methods belong to the "hindsight" detection method, which cannot play an early warning role

Moreover, this manual detection method cannot achieve the purpose of real-time monitoring of the bridge part.

[0003] The current bridge health monitoring system collects a large amount of data. How to diagnose the structural damage of the corresponding monitoring parts through the analysis of the monitoring data is a difficult point in the industry. In order to dig out structural damage from the massive data of bridge health monitoring situation, a feasible method is required

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