Developments for Structural Health Monitoring of Transportation Infrastructure

加拿大和世界各地的研究人员、官员和从业者普遍认为，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。有相当大的努力来恢复和改进这些系统，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，是为基础设施所有者和决策者提供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来测量结构的响应以及操作和加载环境，以跟踪和评估其安全和适用性的性能。传感技术的最新进展使数据收集变得容易；然而，在常规的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重大挑战。因此，拟议研究计划的主要目标是研究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，特别是桥梁。重点领域之一将是开发基于统计模式识别的损害检测方法，并对这些方法进行微调，以便对桥梁进行自动长期监测。由于桥梁受到不断变化的环境和运营条件的影响，这些每日、季节性和年度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，环境变化可能会产生更大的影响。因此，这项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造成的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。这项研究的结果有望帮助加拿大基础设施机构管理基础设施系统，不仅提高其安全性和可靠性，还能在其使用寿命期间优化维护操作，显著节省生命周期成本。

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.