System Identification of Dynamically Sensitive Structures using Wireless Smart Sensor Networks

北美和其他地区的大量建筑和桥梁已经接近或已经超过了设计寿命。许多国家容易受到动态影响(如地震)的影响，因为它们的风险最初被低估了。我们需要评估它们的现状，以便做出合理的工程决策，决定是否更换、修复或改造它们，并预测它们的剩余寿命。这可以通过系统识别来实现，系统识别将从结构中获得的传感器测量结果与数学程序相结合，以产生有关其当前状态的信息。 就在几年前，无线传感器还是一项昂贵的工作，现在已经使大规模建筑上的系统识别变得更容易负担得起。不需要昂贵的布线，并且这些传感器上的本地处理功能允许分层实现算法。然而，无线传感器硬件的进步已经远远超过了我们有效使用和询问测量数据的能力。这是因为，现有的算法对噪声敏感，不是自主的，需要大量的传输负载。 这项提议的主要目的是通过开发一类新的算法来克服这些缺点。这些算法将是分散的、计算轻量级的和自主的，并将使用低传输速率。这将使用信号处理中的两个强大的概念来实现：时频分析和压缩传感。前者将实现自主和分散的实施，而后者将帮助我们减少数据传输。提出了理论研究、实验室研究和现场研究的建议。 这种方法将产生有效和负担得起的状况评估手段，以解决基础设施老化的问题。学生将接受跨学科技能培训，这在加拿大是很有需求的。

Large stocks of buildings and bridges in North America and elsewhere are nearing their design life, or have already surpassed it. Many are vulnerable to dynamic effects (e.g., earthquakes), as their risk was initially under-estimated. We need to assess their current state in order to make sound engineering decisions on whether to replace, rehabilitate or retrofit them, and to predict their remaining life. This is possible through system identification, which combines sensor measurements obtained from the structure and mathematical procedures to yield information regarding its current state. An expensive exercise just a few years ago, wireless sensors have now made system identification on large-scale structures more affordable. There is no need for expensive wiring, and local processing capabilities on these sensors allow for hierarchical implementation of algorithms. Nevertheless, the advances in wireless sensor hardware have far out-paced our ability to effectively use and interrogate measured data. This is because, existing algorithms are sensitive to noise, not autonomous and demand large transmission loads. The main aim of this proposal is to overcome these drawbacks by developing a new class of algorithms. These algorithms will be decentralized, computationally light and autonomous, and will employ low transmission rates. This will be achieved using two powerful concepts in signal processing: time-frequency analysis and compressive sensing. The former will enable autonomous and decentralized implementations, while the latter will help us reduce data transmission. Theoretical, laboratory and field studies are proposed. This approach will lead to effective and affordable means of condition assessment to address the problem of aging infrastructure. Students will be trained in inter-disciplinary skills, which are in demand in Canada.