High Fidelity Probabilistic Structural Health Monitoring

在过去的十年中，基于传感器的结构健康监测已经成为一个重要的研究领域，因为它显示出巨大的生命安全和经济效益，改善和负责任的管理我们老化的民用基础设施。结构健康监测包括检测结构内部的损伤或恶化，确定其位置和严重程度，并最终有助于为结构的未来寿命提供预测。许多重要的土木结构体系都是大型和复杂的，有许多接缝和构件。在这样一个基于相对较少的传感器测量的大型系统的高保真模型中识别损坏和退化的任务是非常具有挑战性的，这些传感器测量本身并不完美。本研究项目的主要目标是以概率形式提供更详细和准确的结构状况估计，这可以集成到基础设施利益相关者更可靠的决策工具中。此外，开发用于非线性、高维系统识别和参数学习的新型算法工具在工程、生物甚至金融等许多领域都引起了极大的兴趣。因此，这项研究不仅可以从其他领域的知识中受益，而且可以为研究领域做出贡献，远远超出了此处动机核心的结构监测背景。在本研究采用的核心框架贝叶斯估计中，算法的前沿在于对噪声的敏感性，例如高斯与非高斯，以及处理具有局部非线性和许多静态参数需要识别的高维问题。克服这些挑战既需要对公认算法的深刻理解，也需要开发增强或新颖的算法工具。在贝叶斯估计中，文献中广泛研究了两种主要的滤波算法。本文将在感兴趣的系统中仔细研究无气味卡尔曼滤波器中使用的高斯假设的后果，同时将介绍在高维系统中表现不佳的粒子滤波器的增强。解决高维问题的策略是基于rao - blackwell化原则和划分方案。离线算法也可以集成在这个框架中，以获得更准确的估计和更低的不确定性。最后，这些方案应在实际的实验数据上进行验证。

In the last decade, sensor based Structural Health Monitoring has become an important area of research as it shows great potential for life-safety and economic benefits for improved and responsible management of our aging civil infrastructure. Structural health monitoring involves the detection of damage or deterioration within a structure, the identification of its location and severity and ultimately it can assist in providing a prognosis for the future life of a structure. Many important civil structural systems are large and complex, with many joints and components. The task of identifying damage and deterioration in a high fidelity model of such a large system based on relatively few sensor measurements which themselves are not perfect is highly challenging. The main goal of this research project is to provide more detailed and accurate estimates of structural condition in a probabilistic format, which can be integrated into more reliable decision-making tools for infrastructure stakeholders. Furthermore, development of novel algorithmic tools for nonlinear, high dimensional system identification and parameter learning are of utmost interest in many fields of engineering, biology and even finance. Thus, this research could not only benefit from knowledge in other fields, but could contribute to research domains well beyond the structural monitoring context at the heart of the motivation here. Within Bayesian estimation, the core framework adopted in this research, the algorithmic frontiers lie in the sensitivity to noise, Gaussian versus non-Gaussian, for example, as well as tackling high dimensional problems with local nonlinearities and many static parameters to be identified. Overcoming these challenges requires both a deep understanding of well accepted algorithms as well as development of enhanced or novel algorithmic tools. In Bayesian estimation, two main filtering algorithms are extensively studied in the literature. The consequences of the Gaussianity assumption used in the unscented Kalman filter will be carefully studied on systems of interest, while enhancements of the particle filter, which behaves poorly in high dimensional systems, will be introduced. Strategies to tackle the high dimensionality issue are based on the Rao-Blackwellisation principle as well as partitioning schemes. Off-line algorithms could also be integrated in this framework to obtain more accurate estimates with lower uncertainties. Finally these schemes should be validated on realistic, experimental data.