Developments for Structural Health Monitoring of Transportation Infrastructure

加拿大和世界各地的研究人员、官员和从业人员普遍认为，老化的民用基础设施系统是我们社会面临的最重大挑战之一。为恢复和改善这些系统作出了相当大的努力，其长期目标是发展一个可持续的基础设施系统。结构健康监测（SHM）被认为是这一长期目标的最关键组成部分之一，是基础设施业主和决策者的客观和创新的决策支持工具。结构健康监测是利用传感技术测量结构沿着运行和荷载环境的响应，以跟踪和评估其安全性和适用性。传感技术的最新进展使数据收集变得容易;然而，在常规SHM应用之前，对测量数据进行适当和有效的分析仍然是一个主要挑战。因此，拟议的研究计划的主要目标是调查和开发新的数据分析技术，用于民用基础设施系统的损坏检测和性能评估，特别强调桥梁。重点领域之一将是开发基于统计模式识别的损伤检测方法，并对这些方法进行微调，以实现桥梁的自动化长期监测。由于桥梁会受到不断变化的环境和运营条件的影响，这些日常，季节和年度的变化可能会掩盖损坏对数据的影响。考虑到加拿大恶劣的天气条件，环境变化甚至可能产生更大的影响。因此，本研究的第二个主要组成部分将侧重于消除由环境和操作影响引起的SHM数据中的不确定性。开发完成后，这些方法将通过实验室和现场实验进行测试。这项研究的成果预计将有助于加拿大基础设施机构与基础设施系统的管理，不仅提高其安全性和可靠性，而且还优化其使用寿命期间的维护操作，提供生命周期成本的显着节省。

It is widely accepted by researchers, officials and practitioners in Canada and all around the world that aging civil infrastructure systems are among the most significant challenges faced by our society. There is a considerable effort to restore and improve these systems with a long-term goal of developing a sustainable infrastructure system. Structural Health Monitoring (SHM) is considered to be one of the most critical components of this long-term goal as an objective and innovative decision-making support tool for infrastructure owners and decision-makers. SHM is the employment of sensing technologies for measuring the responses of a structure along with the operating and loading environment to track and evaluate its performance for safety and serviceability. Recent advances in sensing technologies enable easy data collection; however, proper and efficient analysis of the measured data still stands as a major challenge before routine SHM applications. Therefore, the main objective of the proposed research program is to investigate and develop novel data analysis techniques for damage detection and performance evaluation of civil infrastructure systems, with a special emphasis on bridges. One of the focus areas will be the development of statistical pattern recognition based damage detection methodologies and fine-tuning these methodologies for automated long-term monitoring of bridges. As bridges are subject to changing environmental and operational conditions, the effect of the damage on the data may be masked by these daily, seasonal and yearly changes. Considering the severe weather conditions in Canada, environmental changes may even have larger impacts. Therefore, the second major component of this research will focus on eliminating the uncertainties in the SHM data caused by the environmental and operational effects. After the development, these methodologies will be tested with laboratory and field experiments. The outcomes of this research are expected to help Canadian infrastructure agencies with the management of infrastructure systems not only to increase their safety and reliability but also to optimize maintenance operations during their service life, offering significant savings in life cycle cost.