EAPSI:Quantifying Effects of Sensor Network Reliability on Modal Identification of a Five-Story Steel Structure

EAPSI：量化传感器网络可靠性对五层钢结构模态识别的影响

• 批准号: 1515459
• 负责人: Thomas Matarazzo
• 金额: $ 0.51万
• 依托单位: Matarazzo Thomas J
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1515459&HistoricalAwards=false
• 关键词: EAPSI; Quantifying; Effects; Sensor; Network

Structural engineering researchers and practitioners invest an immense amount of time and money into reliable data acquisition systems with the ambition of obtaining up-to-date information about the true behavior of existing infrastructure. However, even state-of-the-art technologies are susceptible to missing packets, erroneous values, and other malfunctions. The finite reliability of sensors and data acquisition systems disrupts data-driven methods designed to extract important structural information. Furthermore, the likelihood of sensing failures and corresponding incomplete datasets increases during extreme weather events. This award supports research using large-scale experiments to discover the true impact of sensor network reliability on the estimation of important structural features of structural health. The work will be conducted under the mentorship of Professors Masayoshi Nakashima and Masahiro Kurata at the world-renown Disaster Prevention Research Institute at Kyoto University, Japan. The main hypothesis of this project is despite their missing content, there is a substantial amount of crucial features available within incomplete datasets. Without suitable processing methods, this data is often considered damaged beyond repair then partially or fully discarded, leaving important structural information unknown. Structural responses of a five-story steel frame will be measured using a data acquisition system with a low reliability. New techniques that accurately estimate structural modal properties using incomplete datasets will be proposed. Central analytical goals of this project will focus on location-based knowledge in these cases, e.g. the quantification of total spatial information in terms of total network reliability and individual sensor reliability. Consideration of this data class in large-scale structural tests is essential for the development and validation of new computational tools that extract maximal information using measured responses from existing infrastructure. The structural health monitoring methods that support incomplete datasets offer expedited post-event assessments of structural integrity, permitting prompt notification to engineers, local governments, and society. This NSF EAPSI award is funded in collaboration with the Japan Society for the Promotion of Science.

结构工程研究人员和从业人员投入大量的时间和金钱到可靠的数据采集系统中，以获得有关现有基础设施真实行为的最新信息。然而，即使是最先进的技术也容易受到丢失数据包、错误值和其他故障的影响。传感器和数据采集系统的有限可靠性破坏了旨在提取重要结构信息的数据驱动方法。此外，在极端天气事件期间，传感故障和相应的不完整数据集的可能性增加。该奖项支持使用大规模实验的研究，以发现传感器网络可靠性对结构健康的重要结构特征估计的真正影响。这项工作将在日本京都大学世界著名的防灾研究所的中岛正吉教授和仓田正弘教授的指导下进行。 该项目的主要假设是，尽管它们缺少内容，但在不完整的数据集中有大量的关键特征。如果没有合适的处理方法，这些数据通常被认为是损坏无法修复，然后部分或全部丢弃，留下重要的结构信息未知。采用可靠性较低的数据采集系统，对一个五层钢框架结构的结构响应进行了测试。将提出使用不完整数据集精确估计结构模态特性的新技术。在这些情况下，该项目的中心分析目标将集中在基于位置的知识上，例如，在总网络可靠性和单个传感器可靠性方面量化总空间信息。在大规模结构测试中考虑这类数据对于开发和验证新的计算工具至关重要，这些工具使用现有基础设施的测量响应提取最大信息。支持不完整数据集的结构健康监测方法提供了对结构完整性的快速事后评估，允许及时通知工程师，地方政府和社会。这个NSF EAPSI奖是与日本科学促进协会合作资助的。