Bridge Structural Identification for Condition Assessment

该项目的重点是探索结构识别在将当前的半经验实践转变为理性知识基础方面的作用，从而为有效和最佳的基础设施设计运营和管理铺平道路。 将回答有关桥梁结构的结构识别使用的基本问题。 该研究将主要集中在数据处理，质量保证，损伤评估和可靠性评估方面的结构识别。 实验和分析工具，允许有意义的和实际应用的结构识别只是目前出现。 已经对一座试验桥进行了测试，这是一座在辛辛那提地区使用10年、长188 ft、三跨、双车道、整体式桥台、钢筋混凝土桥面钢梁立交桥，以提供所需的试验数据。 另一座桥梁正在建设中，正在安装350多个传感器。 智能监测系统将长期监测加速度、位移、旋转、应变、温度、卡车载荷和环境条件。 除了主动和被动传感器外，还将使用摄影测量图像处理技术。 结构识别实验将包括：模态测试的影响，模态测试的多输入强迫激励，几何监测，交通输入和操作响应，环境输入和环境响应，静态验证级多卡车加载等各种各样的结构识别方法和分析工具将进行研究。

This project focuses on exploring the role of structural identification in transforming the current semi-empirical practice to a rational knowledge-base, thereby paving the way for effective and optimum infrastructure designing operation and management. Fundamental questions pertaining to the use of structural identification for bridge structures will be answered. The research will be primarily focused on structural identification in terms of data processing, quality assurance, damage evaluation, and reliability evaluation. The experimental and analytical tools that permit meaningful and practical applications of structural identification are only presently emerging. A test bridge has already been instrumented which is a 10 year old, 188 ft long, three-span, two lane, integral abutment, reinforced concrete-deck-on-steel-girders overpass in the Cincinnati area to provide needed test data. Another bridge is under construction and is being instrumented with over 350 sensors. Accelerations, displacements, rotations, strains, temperatures, truck-loads and environmental conditions will be monitored over the long-term by intelligent monitoring systems. In addition to active and passive sensors, photogrammetric image-processing technologies will be used. The structural identification experiments will include: modal testing by impact, modal testing by multi-input forced-excitation, geometry-monitoring, traffic-inputs and operating responses, environmental inputs and ambient responses, static proof-level multiple truck-loading, etc. A wide variety of structural identification approaches and analytical tools will be studied.