Bridge modular expansion joint service safety evaluation method and system

By combining dynamic Bayesian networks and real-time monitoring data, the problems of high cost and low accuracy in safety inspection of modular expansion joints on bridges have been solved, enabling rapid and accurate safety assessment and risk warning, and ensuring the safe and stable operation of bridges.

CN122389170APending Publication Date: 2026-07-14SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Filing Date
2026-05-12
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

In the existing technology, the safety inspection of modular expansion joints on bridges relies on manual inspection, which is costly, time-consuming, and lacks objectivity and accuracy, making it difficult to quickly and accurately assess their safety status.

Method used

By combining dynamic Bayesian networks with real-time monitoring data, the safety status of modular expansion joints on bridges is assessed and potential risk chains are traced by constructing dynamic Bayesian networks and using the connection tree algorithm to update posterior probabilities.

Benefits of technology

It enables rapid and accurate safety assessment of modular expansion joints on bridges, real-time updates of safety probabilities, timely detection of potential risks, reduction of the likelihood of safety accidents, rational allocation of operation and maintenance resources, and improvement of the accuracy and efficiency of assessments.

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Abstract

The application discloses a bridge modulus type expansion device service safety evaluation method and system, comprising the following steps: S110, acquiring real-time monitoring data; S120, constructing a dynamic Bayesian network according to a preset Bayesian network structure; S130, inputting the real-time monitoring data into the dynamic Bayesian network; S140, updating the posterior probability of the dynamic Bayesian network by using a junction tree algorithm to obtain the real-time safety probability of the bridge modulus type expansion device. The application can quickly and accurately update the safety state probability of the expansion device by constructing a dynamic Bayesian network and fusing real-time monitoring data, and overcomes the technical problems of long evaluation period and low accuracy caused by relying on manual inspection in the prior art. In addition, the application can also trace back the risk chain through reverse reasoning, provide a scientific basis for formulating targeted operation and maintenance strategies, and thus realize long-term, stable and efficient health monitoring and safety evaluation of the bridge expansion device.
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