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成为一个重要的研究领域，因为它显示出巨大的潜力，为生命安全和经济效益，以改善和负责任的管理我们老化的民用基础设施。结构健康监测涉及检测结构内的损坏或劣化，识别其位置和严重程度，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系统是大型和复杂的，具有许多节点和构件。基于相对较少的传感器测量（其本身并不完美）来识别如此大的系统的高保真度模型中的损坏和劣化的任务是极具挑战性的。该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，这些估计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，开发新的算法工具，非线性，高维系统的识别和参数学习是最感兴趣的工程，生物学，甚至金融等许多领域。因此，这项研究不仅可以受益于其他领域的知识，但可以有助于研究领域远远超出了结构监测的背景下，在这里的动机的核心。在贝叶斯估计，在这项研究中采用的核心框架，算法的前沿在于对噪声的敏感性，高斯与非高斯，例如，以及处理高维问题与局部非线性和许多静态参数被确定。克服这些挑战需要深入了解公认的算法以及开发增强或新颖的算法工具。在贝叶斯估计中，两种主要的滤波算法在文献中被广泛研究。在无迹卡尔曼滤波器中使用的高斯假设的后果将仔细研究感兴趣的系统，而粒子滤波器，在高维系统中表现不佳的增强，将被引入。解决高维问题的策略是基于Rao-Blackwellisation原则以及分区方案。离线算法也可以集成在这个框架中，以获得更准确的估计与较低的不确定性。最后，这些计划应验证现实的，实验数据。

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.