Collaborative Research: Fatigue Damage Prognosis for Slender Coastal Bridges

合作研究：沿海细长桥梁的疲劳损伤预测

• 批准号: 1537121
• 负责人: Wei Zhang
• 金额: $ 28.37万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537121&HistoricalAwards=false
• 关键词: Collaborative; Research; Fatigue; Damage; Prognosis

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. The proposed 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 proposed. The proposed 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 proposed 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 the proposed methodology, coupled structure dynamics model will capture the realistic service and environmental loads during the lifetime span of the bridges.

细长的沿海桥梁基础设施对该地区的经济和生活质量至关重要。沿海桥梁受到包括盐水和大风在内的危险条件的影响。钢的腐蚀是损坏桥梁并降低其承载能力的因素之一。准确的桥梁病害预测和完整性评估将对国家经济发展和公共安全起到重要的促进作用。对复杂退化过程的基本理解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。所提出的研究也将有利于许多其他类型的基础设施，如发电厂，海上结构和管道的安全评估。这项研究的结果有可能减少许多老化基础设施系统由于结构故障而造成的经济和社会损失。此外，该项目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。提出了一种基于最大熵的多尺度腐蚀疲劳损伤预测贝叶斯网络。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，建立了网络中各级节点之间的相互关系。先进的疲劳试验和模拟方法将结合在一起，以物理为基础的预测剩余使用寿命的肋桥。将通过所提议的贝叶斯网络传播未验证性，并将更新和重新评估系统级可靠性。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来更新自己。在所提出的方法中，耦合结构动力学模型将捕捉在桥梁的寿命跨度的现实服务和环境载荷。