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.

本计画的研究目的是发展一个感测器驱动的结构健康预测系统，以进行焊接管节点钢结构的可靠度更新与维修排程。将连续传感器数据流与随机退化模型和决策相结合，建立了基于概率力学的结构健康预测方法。在长期监测系统中的传感器的性能退化将明确占随机方法。将使用贝叶斯和马尔可夫链蒙特卡罗模拟方法更新退化模型。设计了一种基于柔性压电传感器阵列的疲劳裂纹扩展监测系统。 该系统可用于弯曲和平坦的表面，它将特别制定和实验验证的焊接管接头上常见的起重机和公路标志支撑结构的应用。这些成果包括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的设计，利用先进的数字信号处理和系统集成方法，本研究的结果预计将提供一个有前途的方法来评估服务条件，并通过按需维护延长土木结构的寿命。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，如钢桥。研究结果将予以传播，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的研究计划的组成部分。将作出真诚的努力，让合格的代表性不足的学生参与项目研究活动，使他们从拟议的跨学科研究和教育活动中受益。马里兰州大学现有的高中生和实习工程师推广计划将得到利用。