基于机器学习的车辆作用下桥梁局部结构响应预测理论

以桥面结构为代表的桥梁局部结构在设计评估等过程中需重点考虑车辆荷载的作用。实际运营中此类结构对车辆时空分布敏感，且车辆轴载变异显著，造成响应异常复杂。目前规范中的车辆荷载模型（即轴载模型）难以有效考虑这些因素；现有车流模拟技术也无法满足分析的精度要求。本项目基于人工智能方法，探索数据驱动的荷载响应智能分析理论，解决桥梁局部结构响应预测中的基础科学问题。首先，研究车辆驾驶行为规律，基于多智能体建模提出考虑复杂行驶特性和短时变异性的微观车流模拟方法。其次，基于局部结构的精确有限元模型，获取荷载分布及与其相匹配的结构响应，开展机器学习模型构建及其架构优化，实现多响应的结构化输出。最后，基于荷载监测断面的车辆数据，产生响应关注断面的微观车流，预测局部响应的量值范围，实现多区域、多响应的快速预警和评估。研究成果将形成桥梁局部结构响应预测的基础理论和方法，也为进一步揭示局部损伤机理提供数据支撑。

The bridge local structure, which is typically represented by bridge deck, is mainly analyzed and designed by vehicle load model (defined by standard axle load). While in practice, the bridge local structural response has complex characteristics, showing remarkable sensitivity to the temporal and spatial distribution of vehicle load, which results in significant variability of response. It is difficult to cover such variability with the vehicle load model adopted by the current design code. Although the random traffic simulation technology has gained substantial achievements in the recent year, the accuracy of the existing methods can still not meet the analysis of the bridge local structural response. Therefore, based on the artificial intelligence method, this research explores the data-driven intelligent analysis theory of structural response, in a bid to solve this problem. Firstly, behavior rules of the vehicle in various driving environments are studied. On the basis of the multi-agent modeling, a micro-scale traffic flow simulation method, which considers complex driving characteristics and short time variability, is proposed. Secondly, with the help of accurate FEM of local structure, we can obtain adequate data of load distribution and it corresponding local response. Furthermore, research on machine learning model construction and framework optimization is executed to implement the structured output of multiple local structural responses. Finally, with reference to the traffic data from the monitoring section, the micro-scale traffic flow is simulated corresponding to the concerned sections. Such traffic flow is further utilized as the estimator of the value range of local structural responses. On the foundation of the above research, quick warning and assessment of multiple responses in multi-zone scenario are sought to be realized in engineering applications. The achievements in this research will form instrumental theories and approaches for prediction of bridge local structural response, and provide fundamental data for the further revealing of the mechanism of local damage.