The health monitoring technology for bridge prestressed structures has been upgraded, enabling smart full-lifecycle operation and maintenance.
2019-12-10
Recently, a new-generation bridge prestressed structure health monitoring system, developed by a smart technology company, completed its upgraded deployment on the Hangzhou Bay Bridge. This system integrates cutting-edge technologies such as fiber optic sensing, big data analytics, and artificial intelligence, enabling intelligent full-lifecycle maintenance and operation for bridge prestressed structures and significantly enhancing the safety assurance capabilities of bridges. As a critical transportation hub in China, the Hangzhou Bay Bridge is influenced by various factors including sea winds, tides, and vehicle loads, making the health status of its prestressed structures directly impact the operational safety of the bridge.
Recently, a new-generation bridge prestressed structure health monitoring system, developed by a smart technology company, completed its upgraded deployment on the Hangzhou Bay Bridge. This system integrates cutting-edge technologies such as fiber optic sensing, big data analytics, and artificial intelligence, enabling intelligent full-lifecycle maintenance and operation for bridge prestressed structures and significantly enhancing the safety assurance capabilities of bridges. As a critical transportation hub in China, the Hangzhou Bay Bridge is influenced by various factors including sea winds, tides, and vehicle loads, making the health status of its prestressed structures directly impact the operational safety of the bridge.
According to the system’s development lead, the new-generation monitoring system, building upon existing monitoring technologies, incorporates additional equipment such as fiber Bragg grating stress sensors and temperature sensors. This enables real-time and precise monitoring of multiple parameters, including prestressed steel strand stress, anchor conditions, and structural temperatures, with monitoring accuracy improved by 50% compared to traditional systems. The system’s backend is equipped with an AI-powered analytical model capable of automatically identifying abnormal data, predicting the development trends of structural defects, and generating tailored maintenance recommendations. Managers can monitor the health status of the bridge’s prestressed structures in real time through a visual platform. The upgraded system has successfully identified three potential stress anomaly points, prompting timely reinforcement measures by the construction team and preventing potential safety hazards. The application of this system not only reduces bridge maintenance costs but also provides robust support for the long-term safe operation of the bridge, with potential for broader adoption in large-scale bridge projects nationwide.
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