Fiber Optic Methods for Structural Health Monitoring based on Dynamic Curvature and Displacement

Abstract

With the growing challenge of aging infrastructure and the increasing cost for replacement and repair, structural health monitoring (SHM) offers an approach to address these challenges. SHM is the process of sensing parameters of a structural system over time, either continuously or periodically, with the goal of better understanding a structures behavior and determining the current state of health and performance. The overarching aim of this research is to create and analyze strain-based curvature and displacement SHM methods for beam-like structures. This thesis will focus on the use of long-gage fiber Bragg grating (FBG) sensors as they offer numerous benefits compared to other sensors currently available, such as moderate cost, multiplexing capabilities and the ability for both static and dynamic monitoring. The SHM research field has grown considerably in the last two decades. Taking into account this growth, this thesis first presents an analysis of the structure and evolution of the SHM research field over the past 15 years through a bibliometric analysis, to assess the field and get insights about the position of FBGs in the field. In particular, long-gage FBG strain sensors allow for the instrumentation of large areas of a structure which helps enable its global monitoring. Based on these sensors, a novel, strain-based damage sensitive feature was identified, the normalized curvature ratio (NCR), and its performance in damage detection was evaluated. In addition, a comparative analysis of the accuracy of stain-based displacement methods was carried out, and their performance in serviceability assessment was evaluated. The research included analytical and numerical modeling, small-scale laboratory tests, and applications to and validation on a full-scale in-service structure.