Developing an Advanced Hybrid System of Structural Health Monitoring

工程研究界迫切需要一个可靠、高效、鲁棒的结构健康监测系统来支持桥梁等老化结构的监测。已经开发了许多不同的基于振动的方法来检测基础设施中的损坏，这些方法大致分为基于数据和基于模型的方法。然而，损伤检测方法，可以占这样的问题的复杂性，如环境温度的变化和跨桥梁的温度分布不均匀，仍然需要开发。就准确性、损害的定位和量化以及成本效益而言，没有最好的方法。大多数现有的研究只集中在一个或两个任务，如检测或检测和定位的损害。检测、定位、量化和补偿环境影响的一个首选解决方案是将基于数据和模型的方法结合起来。这将提供量化的损坏信息，以帮助工程师做出适当的维护决策。这些问题正在推动一个新的系统的集成混合损伤检测系统，可以解决现有方法的缺点的发展。 我的研究项目主要集中在开发创新的混合方法，结构损伤检测系统，联合收割机结合数据和基于模型的方法，以利用两者的固有优势。基于数据的方法使用概率/非概率模式分类器或有监督和无监督机器学习方法来将损伤敏感特征（诸如自然频率和模态形状）分类为完好的或受损的。基于模型的方法使用目标结构的物理模型，通常应用有限元方法。通过将这两种方法相结合并共享来自传感器的测量数据，可以获得一种混合方法，与传统的独立方法相比，该方法将以具有成本效益的方式提供量化的损伤信息以及早期检测和定位。然而，由于噪音、传感器故障和复杂的环境影响，误报的可能性很高，这意味着工程师仍需进行现场访问，以确认是否发生了损坏。为了应对这一挑战，将开发一种新的基于计算机视觉的损坏检测方法，使用低分辨率相机（类似于智能手机中使用的相机），先进的图像处理技术和基于数据的方法中使用的机器学习方法，将图像分类为“完整”或“损坏”。这种损伤检测方法最终将与新的混合动力系统相结合。新的计算机视觉混合损伤检测系统将通过提供结构损伤的明确视觉记录和最佳共享测量的传感器数据来提供可靠，准确和具有成本效益的信息。

There is an essential and urgent need in engineering research communities for a reliable, efficient and robust structural health monitoring system to support the monitoring of aging structures like bridges. Many different vibration-based methods have been developed to detect damage in infrastructure, which are broadly categorized as data- and model-based approaches. However, damage detection methods that can account for the complexity of such problems as ambient temperature changes and non-uniform distribution of temperature across the bridges, still need to be developed. There is no best method in terms of accuracy, localization and quantification of damage, and cost efficiency. Most existing research has focused on only one or two tasks, such as detection or detection and localization of damage. A preferred solution to detect, localize, quantify and compensate for environmental effects would be to integrate both data and model-based methods. This would provide quantified damage information to help engineers make appropriate maintenance decisions. These concerns are driving the development of a new system of integrated hybrid damage detection systems that can address the shortcomings of existing methods. My research program is focused on the development of innovative hybrid approaches to structural damage detection systems that combine data- and model-based methods in order to leverage the inherent strengths of both. The data-based method uses probabilistic/non-probabilistic pattern classifiers or supervised and unsupervised machine learning approaches to classify damage-sensitive features such as natural frequencies and mode shapes as intact' or damaged'. The model-based method uses physics model of the target structure, typically applying a finite element method. By combining the two approaches and sharing measured data from sensors, a hybrid method can be obtained that will provide quantified damage information with early detection and localization in a cost-efficient manner as compared with traditional independent approaches. However, the high possibility of false alarms due to noises, sensor malfunctions and complex environmental effects means that engineers would still have to make on-site visits to confirm that damage has occurred. To address this challenge, a novel computer-vision-based damage detection method will be developed using low resolution cameras (similar to those used in smartphones), advanced image processing techniques and machine learning methods used in the data-based approach to classify images as intact' or damaged'. This damage detection method will ultimately be combined with the new hybrid system. The new hybrid damage detection system with computer vision will supply information that is reliable, accurate and cost-efficient by providing an explicit visual record of structural damage and optimally sharing measured sensor data.