Sensor-Driven Structural Health Prognosis System for Continuous Reliability Assessment of Tubular Steel Structures

用于管状钢结构连续可靠性评估的传感器驱动结构健康预测系统

• 批准号: 1031304
• 负责人: Yunfeng Zhang
• 金额: $ 24万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1031304&HistoricalAwards=false
• 关键词: Sensor; Driven; Structural; Health; Prognosis

The research objective of this project is to develop a sensor-driven structural health prognosis system for reliability updating and maintenance scheduling of steel structures with welded tubular joints susceptible to fatigue-induced cracking. A probabilistic mechanics based structural health prognosis procedure that integrates continuous sensor data stream with stochastic degradation model and decision making will be established. Performance degradation of sensors in long term monitoring systems will be explicitly accounted for in a stochastic approach. The degradation model will be updated using the Bayesian and Markov Chain Monte Carlo simulation methods. A fatigue crack growth monitoring system utilizing the flexible piezoelectric paint sensor arrays will designed. The system can be used on curved and flat surfaces; it will be especially formulated and experimentally validated for applications on welded tubular joints commonly found in cranes and highway sign support structures. Deliverables include the design of fundamental components and technologies for sensor-driven structural health prognosis and risk analysis tools utilizing advanced digital signal processing and system integration methods.The results of this research are expected to provide a promising method to assess the service condition and for extending the life of civil structures through on-demand maintenance. Example applications include cranes, signal support structures and offshore structures, as well as other metal structures such as steel bridges. The results will be disseminated to facilitate the technology transfer that will enable smart renewal and maintenance of civil infrastructures. Education and professional training of graduate students are integral components of the proposed research program. Sincere attempts will be made to involve qualified underrepresented students in the project research activities to benefit them from the proposed interdisciplinary research and education activities. Existing outreach programs for high school students and practicing engineers at the University of Maryland will be leveraged.

该项目的研究目标是开发一种传感器驱动的结构健康预测系统，用于具有易受疲劳裂纹影响的焊接管接头的钢结构的可靠性更新和维护计划。将建立一个基于概率力学的结构健康预测程序，将连续传感器数据流与随机退化模型和决策相结合。长期监测系统中传感器的性能下降将通过随机方法明确地加以考虑。退化模型将使用贝叶斯和马尔可夫链蒙特卡罗模拟方法进行更新。将设计一种利用柔性压电涂料传感器阵列的疲劳裂纹扩展监测系统。 该系统可用于曲面和平面；它将专门针对起重机和公路标志支撑结构中常见的焊接管接头的应用进行配制和实验验证。可交付成果包括利用先进的数字信号处理和系统集成方法，设计用于传感器驱动的结构健康预测和风险分析工具的基本组件和技术。这项研究的结果预计将为评估服务状况和扩展服务提供一种有前景的方法。通过按需维护来延长土木结构的使用寿命。示例应用包括起重机、信号支撑结构和海上结构，以及其他金属结构，例如钢桥。结果将被传播，以促进技术转让，从而实现民用基础设施的智能更新和维护。研究生的教育和专业培训是拟议研究计划的组成部分。我们将真诚地尝试让合格的代表性不足的学生参与项目研究活动，以使他们从拟议的跨学科研究和教育活动中受益。将利用马里兰大学针对高中生和执业工程师的现有外展计划。