Non-Destructive Evaluation of Critical Bridge Components

9422696 Belyschko对钢桥和其他钢设施进行健康监测和评估，如果用于评估安全性和可靠性，并通过及时维修延长剩余使用寿命，将带来巨大的潜在好处。该项目涉及开发高性能计算工具，用于从无损评估（NDE）传感设备的输出中识别关键桥梁组件中的缺陷，并评估这些缺陷对组件使用寿命的影响。将开发高性能计算工具，从无损检测传感器的输出中提供缺陷的特征，并评估组件的可靠性。拟议的研究将以一种综合办法处理这一目标，其中将包括下列任务：将超声QNDE方法应用于桥梁原位健康监测；2. 利用神经网络来解决裂缝识别和表征的逆问题；3. 开发具有一个或多个裂缝的详细模型的高保真波方程正演解，这是识别过程所需要的；由于这些问题的规模很大，将使用并行计算；4. 根据已识别的裂纹来估计部件的可靠性。整体模型的测试将在测试原型和实际桥梁上进行。各州和一些感兴趣的公司将成立工作组，以促进研究成果的传播。

9422696 Belyschko Health monitoring and assessment of steel bridges and other steel facilities offers tremendous potential benefits if used to asses the safety and reliability and to extend the remaining useful life by timely repairs. This project involves the development of high performance computing tools for identifying flaws in critical bridge components form the output of nondestructive evaluation (NDE) sensing devices and assessing the impact of these flaws on the useful life of the component. High performance computing tools will be developed to provide a characterization of flaws from the output of NDE sensors and asses the reliability of the component. The proposed research addresses this objective in an integrated approach which will involve the following tasks: 1. adapting ultrasonic QNDE methods to in situ health monitoring of bridges; 2. the solution of the inverse problem of identifying and characterizing the crack, which will be accomplished with the aid of neural networks; 3. development of high fidelity forward solutions of the wave equation for detailed models with one or more cracks, which are needed for the identification process; because of the large size of these problems, parallel computations will be used; 4. estimating the reliability of a component in terms of the crack which is identified. Testing of the overall model will take place on test prototypes and on actual bridges. Working groups form various states and several interested companies will be set up to facilitate dissemination of the research results to practice.