Cost-effective Rehabilitation of Civil Infrastructure using Limited Sensors

使用有限的传感器经济高效地修复民用基础设施

• 批准号: RGPIN-2015-06157
• 负责人: Sadhu, Ayan
• 金额: $ 1.75万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679610
• 关键词: Cost; effective; Rehabilitation; Civil; Infrastructure

Even the best-designed, most carefully built structures, such as buildings and bridges, eventually weaken. Whether a structure fails over time due to environmental conditions or use, or suddenly, because of a natural disaster such as an earthquake or strong wind, failure has significant human and societal consequences. Detecting damage while a structure is in use both helps avoid safety risks and minimizes the economic costs of suspending access to assess damage. Recently, vibration based damage detection (VBDD) methods, an offshoot of structural health monitoring, have shown promise in efforts to rehabilitate civil infrastructure. In VBDD, the presence of damage and its severity in a structure is assessed through detecting unusual symptoms in vibration measurements, and then rehabilitation is undertaken using appropriate decision making tools. However, because of the high cost of sensors and their installation issues, it is not always feasible to install sensors at every critical location in a large-scale structure. The proposed research will develop smart and cost-effective technology to perform early damage diagnosis and rehabilitation using limited numbers of vibration sensors.***Current tools for damage detection and maintenance use a wide range of signal processing methods. Recent development of blind source separation (BSS) by the applicant as a powerful class of signal decomposition methods makes them a promising candidate for VBDD. Recently, BSS has been successfully demonstrated in linear structures in a wide range of challenging situations. In the proposed research, BSS algorithms will be expanded to develop sophisticated damage detection algorithms for deteriorating (i.e., nonlinear) structures using a fewer number of vibration measurements. Various decision making approaches, relating to sensor densities, distributed sensing, optimal sensor placement, retro-fitting and control strategies, will be considered. These algorithms will result in cost-effective rehabilitation and technology-enabled maintenance of deteriorating civil structures. ***Such a multidisciplinary research and development will advance efforts to attain sustainable and resilient infrastructure. Researchers and students will develop diverse skills and experience in fields such as structural engineering, mechanical engineering, systems design and decision analysis. This program will develop several research projects with a tangible impact on the smart infrastructure community.**

即使是设计得最好、建造得最仔细的结构，如建筑物和桥梁，最终也会削弱。无论结构是由于环境条件或使用而随着时间的推移而失效，还是由于地震或强风等自然灾害而突然失效，失效都会对人类和社会产生重大影响。在结构使用中检测损坏既有助于避免安全风险，又能最大限度地减少暂停访问以评估损坏的经济成本。最近，基于振动的损伤检测（VBDD）方法，结构健康监测的一个分支，已显示出承诺，在努力恢复民用基础设施。在VBDD中，通过检测振动测量中的异常症状来评估结构中损坏的存在及其严重程度，然后使用适当的决策工具进行修复。然而，由于传感器的高成本及其安装问题，在大型结构中的每个关键位置安装传感器并不总是可行的。拟议的研究将开发智能和具有成本效益的技术，使用有限数量的振动传感器进行早期损伤诊断和康复。当前用于损伤检测和维护的工具使用广泛的信号处理方法。申请人最近开发的盲源分离（BSS）作为一类强大的信号分解方法，使其成为VBDD的有希望的候选者。最近，BSS已被成功地证明在线性结构中的广泛的挑战性的情况。在拟议的研究中，BSS算法将扩展到开发复杂的损伤检测算法，用于恶化（即，非线性）结构使用较少数量的振动测量。各种决策方法，有关传感器密度，分布式传感，最佳传感器的位置，改造和控制策略，将被考虑。这些算法将导致具有成本效益的修复和技术支持的日益恶化的土木结构的维护。* 这种多学科研究和开发将推动实现可持续和有复原力的基础设施的努力。研究人员和学生将在结构工程，机械工程，系统设计和决策分析等领域发展各种技能和经验。该计划将开发几个研究项目，对智能基础设施社区产生切实影响。