Indirect Bridge Health Monitoring Using Moving Vehicles

本研究的目的是利用从一组配备传感器的车辆收集的数据，对大量桥梁进行准确、快速、几乎连续和经济有效的评估，这些传感器能够捕捉车辆与桥梁之间的动态相互作用。这笔拨款为开发一种评估桥梁健康状况的新方法提供资金，该方法使用带有车载传感器的车辆来收集所经过桥梁的状况信息。桥梁的动力特性受到裂缝，腐蚀和冻结轴承形式的损伤的影响。这项研究的第一个前提是，这些变化将从大量车辆在桥上行驶时收集的动态响应中检测到。第二个前提是对桥梁的损坏类型、位置和程度进行分类。新方法将使用多分辨率（MR）信号处理和模式识别算法来检测和分类桥梁损伤。如果取得成功，这项研究的结果将有利于桥梁当局，因为它将导致开发一种新的间接评估方法，用于使用相同的仪器车辆监测大量桥梁的健康状况。该方法将通过提供一种高效且更具成本效益的方法来改善桥梁整体结构状况的管理，从而产生重大的经济影响。这项研究的结果也将导致新的信号处理算法，将捕获从车辆收集的信号。此外，在项目中获得的知识将有助于确定这种方法对不同类型结构的适用性。该研究项目将直接涉及两名博士研究生和一些本科生，其经验和见解将在卡内基梅隆大学和匹兹堡大学的课程和演示中进行转移。

The objective of this research is to provide accurate, rapid, nearly continuous, and cost-effective assessments of a large population of bridges using the data collected from a set of vehicles equipped with sensors able to capture the dynamic interaction between the vehicles and the bridge. This grant provides funding for the development of a new approach for assessing the health of bridges that uses vehicles with on-board sensors to collect condition information about the bridges over which they travel. The dynamic characteristics of the bridge are affected by the damage in the form of cracks, corrosion, and frozen bearings. The first premise of this research is that these changes will be detectable from the dynamic responses collected from a large number of vehicles travelling over the bridge. A second premise is that the type, location and extent of the damage on the bridge will be classified. The new approach will use multiresolution (MR) signal processing and pattern recognition algorithms to detect and classify bridge damage.If successful, the results of this research will be beneficial to bridge authorities by leading to the development of a new indirect assessment method for monitoring the health of a large number of bridges using the same instrumented vehicles. The method will have a significant economic impact by providing an efficient and more cost effective method to improve the management of the overall structural condition of bridges. The results of this research will also lead to new signal-processing algorithms that will capture signals collected from vehicles. In addition, knowledge gained during the project will be useful for determining the applicability of this approach to different types of structures. The experience and the insights provided by this research project, which will directly involve two PhD graduate students and a number of undergraduate students, will be transferred in courses and demonstrations both at Carnegie Mellon University and the University of Pittsburgh.