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: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613979
• 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.

即使是设计最好、建造最仔细的建筑，如建筑物和桥梁，最终也会变得脆弱。无论是随着时间的推移，结构因环境条件或使用情况而失效，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造成重大后果。在结构使用期间检测损坏既有助于避免安全风险，又可以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)方法作为结构健康监测的一个分支，在修复民用基础设施方面显示出了良好的前景。在VBDD中，通过检测振动测量中的异常症状来评估结构中是否存在损伤及其严重程度，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成本效益高的技术，以使用有限数量的振动传感器进行早期损伤诊断和修复。 目前用于损伤检测和维护的工具使用广泛的信号处理方法。盲源分离(BSS)作为一类强有力的信号分解方法，使其成为VBDD的一种有前途的候选方法。最近，盲源分离已成功地应用于各种具有挑战性的情况下的线性结构中。在拟议的研究中，盲源分离算法将被扩展，以开发复杂的损伤检测算法，使用较少的振动测量来检测劣化(即非线性)结构。将考虑各种决策方法，涉及传感器密度、分布式传感、最优传感器布置、回代和控制策略。这些算法将导致具有成本效益的修复和利用技术对不断恶化的土木结构进行维护。 这种多学科的研究和开发将推动实现可持续和有弹性的基础设施的努力。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不同的技能和经验。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。