Indirect Bridge Health Monitoring Using Moving Vehicles

本研究的目的是提供准确，快速，几乎连续的，并具有成本效益的大量人口的桥梁使用收集的数据从一组车辆配备传感器能够捕捉车辆和桥梁之间的动态相互作用。这笔赠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收集有关其行驶的桥梁的状况信息。桥梁的动力特性受到裂缝、腐蚀和冻结支座等形式的损伤的影响。这项研究的第一个前提是，这些变化将是可检测的动态响应从大量的车辆行驶在桥上。第二个前提是对桥梁损坏的类型、位置和程度进行分类。新的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damage.If成功的，这项研究的结果将有利于桥梁当局通过导致一个新的间接评估方法的发展，监测健康的大量桥梁使用相同的仪器车辆。该方法将通过提供一种有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，在项目期间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。 该研究项目提供的经验和见解将直接涉及两名博士研究生和一些本科生，将在卡内基梅隆大学和匹兹堡大学的课程和演示中转移。

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.