CAREER: Risk Monitoring of Civil Infrastructures Using Correlated Change Patterns in Spatiotemporal Data

职业：使用时空数据中的相关变化模式对民用基础设施进行风险监测

• 批准号: 1454654
• 负责人: Pingbo Tang
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454654&HistoricalAwards=false
• 关键词: CAREER; Risk; Monitoring; Civil; Infrastructures

This Faculty Early Career Development (CAREER) Program grant pioneers a change-based risk analysis method that uses diverse spatiotemporal data for predictive defect detection of aging civil infrastructure systems and early warning of structural collapse. Many failures of bridges, marine structures, and other large-scale facilities often can be forecast by gradual changes in their appearances. Civil engineers have found that certain changes across multiple structures always occur together and form collective patterns that predict collapses. For example, decaying steel structures often have collective crack developments around components under cyclic loads. Maintenance agencies can collect various spatiotemporal data, such as images, for monitoring structures and predicting behaviors of structural systems. Unfortunately, current change analysis practices are manual and tedious, so they cannot recover collective patterns from heterogeneous data. This award is for fundamental research in the development of an automated process for detecting changes across heterogeneous data and predicting structural collapse. The developed procedure will enable automated, data-driven analysis of the decays in civil infrastructure systems. Detailed data-driven change analyses of decaying structures have applications in the domains of infrastructure management, aerospace engineering, biomedical engineering, and material science. Therefore, results from this research will broadly benefit the American economy and society. In addition, findings from this project enable wider, global efforts related to change-based risk analysis of civil infrastructures through domestic and international collaborations. This research will aid workforces capable of handling diverse spatiotemporal data for proactive infrastructure management. Finally, change analysis games developed in this effort are to be integrated into the engineering curriculum, K-12 summer workshops, and industry outreach activities. A focus of this award is to engage people from underrepresented groups, especially Hispanics and Native Americans.Existing change analysis algorithms calculate deviations of data points from their nearest neighbors in other data sources. Such neighborhood searching cannot reliably track both global displacements and local deterioration of objects across heterogeneous data. That limitation impedes engineers from characterizing change patterns for predictive defect analysis of structural systems. This research resolves this difficulty through matching isomorphic spatiotemporal patterns across data sources to track both global and local changes of objects, and then correlating these changes across multiple structures for collapse prediction. Specifically, the research effort: 1) examines algorithms that hierarchically match similar spatiotemporal patterns in order to identify corresponding parts of data; 2) investigates algorithms that use corresponding parts of data to detect and classify changes; 3) uses spatial statistical algorithms to discover patterns of correlated changes of multiple structures; and 4) identifies patterns of changes that have strong correlations with collapses. The contributions include: 1) a new method that predicts structural failures through analysis of spatiotemporal change patterns of multiple structures and 2) computationally efficient algorithms for reliably detecting, classifying, and correlating changes across heterogeneous data. Developed techniques will be validated using field data collected from twenty-five structures within the United States, China, Korea, and Hong Kong, along with data from the literature and civil infrastructure data repositories.

这项教师早期职业发展(Career)计划拨款开创了一种基于变化的风险分析方法，该方法使用不同的时空数据来预测老化的民用基础设施系统的缺陷，并对结构倒塌进行早期预警。桥梁、海洋结构和其他大型设施的许多故障往往可以通过其外观的逐渐变化来预测。土木工程师发现，跨越多个结构的某些变化总是同时发生，并形成预测坍塌的集体模式。例如，在循环荷载作用下，腐烂的钢结构通常会在构件周围产生集体裂缝。维护机构可以收集各种时空数据，如图像，用于监测结构和预测结构系统的行为。不幸的是，当前的变更分析实践是手动的、乏味的，因此它们不能从异类数据中恢复集合模式。该奖项是对开发自动化过程的基础研究，该过程用于检测跨异类数据的变化并预测结构崩溃。开发的程序将能够对民用基础设施系统中的衰败进行自动化的、数据驱动的分析。腐蚀结构的详细数据驱动变化分析在基础设施管理、航空航天工程、生物医学工程和材料科学等领域都有应用。因此，这项研究的结果将广泛惠及美国经济和社会。此外，该项目的调查结果有助于通过国内和国际合作，开展更广泛的全球努力，对民用基础设施进行基于变化的风险分析。这项研究将有助于能够处理各种时空数据的工作人员进行主动的基础设施管理。最后，在这项工作中开发的变化分析游戏将被纳入工程课程、K-12暑期讲习班和行业推广活动。该奖项的一个重点是吸引来自代表性不足群体的人们，特别是西班牙裔和美洲原住民。现有的变化分析算法计算数据点与其他数据源中最近邻居的偏差。这种邻域搜索不能可靠地跟踪对象在异质数据上的全局位移和局部劣化。这一限制阻碍了工程师对结构系统的预测性缺陷分析的变化模式进行表征。该研究通过匹配跨数据源的同构时空模式来跟踪对象的全局和局部变化，然后跨多个结构关联这些变化以进行崩溃预测，从而解决了这一难题。具体地说，研究工作：1)检查分层匹配相似时空模式以识别数据的相应部分的算法；2)研究使用数据的相应部分来检测和分类变化的算法；3)使用空间统计算法来发现多个结构的相关变化的模式；以及4)识别与坍塌具有强烈相关性的变化的模式。其贡献包括：1)一种通过分析多个结构的时空变化模式来预测结构失效的新方法；2)计算高效的算法，用于可靠地检测、分类和关联跨异质数据的变化。开发的技术将使用从美国、中国、韩国和香港的25个建筑收集的现场数据，以及来自文献和民用基础设施数据库的数据进行验证。