A Statistical Mapping and Anomaly Warning Method for Bridge Temperature Response

桥梁温度变化对其结构性能有重大影响，但现有监测系统准确性不足。本研究提出基于温度响应统计参数映射模型的桥梁服役状态异常预警方法，通过提取温度数据统计参数，结合多项式回归模型和遗传算法，构建了创新的桥梁健康监测技术框架。研究以典型夏季桥梁为例，模拟结果揭示了温度变化的复杂梯度和时滞特性。该方法为基础设施长期安全监测提供了新的技术思路，有望显著提高桥梁健康监测系统的可靠性。Temperature variations significantly impact bridge structural performance, yet existing monitoring systems lack accuracy. This study proposes an anomaly early warning method for bridge service conditions based on a temperature response statistical parameter mapping model. By extracting temperature data statistical parameters and combining polynomial regression and genetic algorithms, an innovative bridge health monitoring technology framework is constructed. Taking a typical summer bridge as an example, simulation results reveal the complex temperature gradient and time-lag characteristics. The method provides a new technical approach for long-term infrastructure safety monitoring and is expected to significantly improve the reliability of bridge health monitoring systems.