Collaborative Research: Fatigue Damage Prognosis for Slender Coastal Bridges

沿海细长桥梁的基础设施对该地区的经济和生活质量至关重要。沿海桥梁受到危险条件的影响，包括咸水和大风。钢材的腐蚀是破坏桥梁、降低桥梁承载能力的因素之一。对桥梁进行准确的预测和完整性评估，将大大提高国家的经济发展和公共安全。从根本上了解复杂的退化过程将使结构退化的可靠预测成为可能，并使桥梁维护的系统决策成为可能。这项研究还将有助于其他类型的基础设施，如发电厂、海上结构物和管道的安全评估。这一研究成果有可能减少因许多老化基础设施系统的结构故障而造成的经济和社会损失。此外，该项目还可以对教育产生影响，例如培养研究生、本科生和K-12学生对结构和海岸工程的兴趣。提出了一种新的基于最大熵的贝叶斯网络用于沿海细长桥梁多尺度腐蚀疲劳损伤预测。该框架融合了材料层、结构层和系统层的信息和知识，用于概率预测和可靠性评估。通过耦合动力分析和腐蚀-疲劳损伤分析，建立了网络中不同层次节点之间的相互关系。先进的疲劳试验和模拟方法将结合起来，对肋桥的剩余使用寿命进行基于物理的预测。不确定性将通过贝叶斯网络传播，系统级可靠性将得到更新和重新评估。贝叶斯网络可以用来自实验测量、现场观察和历史经验的信息进行自我更新。在该方法中，耦合结构动力学模型将反映桥梁在整个寿命周期内的实际使用和环境荷载。

Infrastructure of slender coastal bridges is critical for the economy and the quality of life for people living in that region. Coastal bridges are subjected to hazardous conditions including salt water and high winds. Corrosion of steel is one of the items that deteriorates the bridges and reduces their capacity to carry the load. Accurate prognosis and integrity evaluation of bridges will significantly enhance the nation's economic development and public safety. The fundamental understanding of the complex degradation process will enable reliable prediction of structural deterioration and enable the systematic decision-making for maintenance of bridges. This research will also benefit the safety evaluation of many other types of infrastructures, such as power plants, offshore structures, and pipelines. Outcome of this research has potential to reduce the economic and social losses due to structural failures in many aging infrastructure systems. In addition, this project can have impact on education, such as cultivating interests of graduate, undergraduate and K-12 students in structural and coastal engineering. A novel maximum entropy-based Bayesian network for multi-scale corrosion-fatigue damage prognosis for slender coastal bridges is planned. The framework fuses the information and knowledge from the material level, the structural level, and the system level for the probabilistic prognosis and reliability assessment. The inter-correlations among different levels of nodes in the network are developed by using coupled dynamic analysis and corrosion-fatigue damage analysis. Advanced experimental testing for fatigue and simulation methods will be combined together for the physics-based prediction of remaining useful life of costal bridges. Uncertainties will be propagated through the Bayesian network and the system level reliability will be updated and reassessed. The Bayesian network can update itself with information from experimental measurements, field observation, and historical experiences. In this methodology, coupled structure dynamics model will capture the realistic service and environmental loads during the lifetime span of the bridges.