Ms. Kaitlyn S. Kliewer Profile

Kaitlyn S. Kliewer

at Princeton Univ

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Publications (3)

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Proceedings Article | 1 April 2019 Presentation

Damage detection capabilities of strain-based displacement methods in beam-like structures (Conference Presentation)

Kaitlyn Kliewer, Branko Glisic

Proceedings Volume 10972, 109721H (2019) https://doi.org/10.1117/12.2515385

KEYWORDS: Damage detection, Fiber Bragg gratings, Sensors, Bridges, Safety, Numerical analysis, Aluminum

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This research presents an evaluation of the damage detection capabilities of displacements calculated from strain measurements in beam-like structure. Displacements can provide useful information for the monitoring and assessment of a bridges health and safety. The displacement of a structure is correlated with the curvature of a structure, so any unusual behavior of the structure that alters the curvature will also affect the displacement of the structure. Additionally, monitoring the displacement of a structure is useful for evaluating service limits as excessive displacement are uncomfortable to users and can cause damage to surrounding structures. Direct displacement monitoring of a real-life structure can be challenging, especially for long-term measurements. Because of this, the focus of this research is on in-direct displacement monitoring using strain-based displacement estimation methods with FBG strain sensors. A numerical analysis was initially performed to evaluate the potential of the methods, which illustrated the damage detection capabilities for a strain-based displacement method. Additionally, small-scale laboratory tests were performed using an aluminum beam instrumented with FBG strain sensors. The beam was tested under static and dynamic loading conditions as well as a cantilever and simply supported boundary conditions. Increasing levels of damage were applied to the beam by reducing the cross-section of the beam in one location to represent a crack. Both an evaluation of the method for damage sensitivity and a comparison with additional strain-based damage detection methods are performed.

Proceedings Article | 5 April 2017 Presentation + Paper

Application of the normalized curvature ratio to an in-service structure

Kaitlyn Kliewer, Branko Glisic

Proceedings Volume 10170, 101702K (2017) https://doi.org/10.1117/12.2260660

KEYWORDS: Sensors, Fiber Bragg gratings, Structural health monitoring, Damage detection, Fiber optics sensors, Structural monitoring, Bridges, Buildings, Civil engineering

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Fiber optic sensors (FOS) offer numerous advantages for structural health monitoring. In addition to being durable, lightweight, and capable of multiplexing, they offer the ability to simultaneously monitor both static and dynamic strain. FOS also allow for the instrumentation of large areas of a structure with long-gages sensors which helps enable global monitoring of the structure. Drawing upon these benefits, the Normalized Curvature Ratio (NCR), a curvature based damage detection method, has been developed. This method utilizes a series of long-gage fiber Bragg grating (FBG) strain sensors for damage detection of a structure through dynamic strain measurements and curvature analysis. While dynamic SHM methods typically rely up frequency and acceleration based analysis, it has been found that strain and curvature based analysis may be a more reliable means for structural monitoring. Previous research was performed through small scale experimental testing and analytical models were developed and provided promising results for the NCR as a potential damage sensitive feature. Based on this success, this research focuses on the application of the NCR to an existing in-service structure, the US202/NJ23 highway overpass located in Wayne, NJ. The overpass is currently instrumented with a series of long-gage FBG strains sensors and periodic strain measurements for dynamic events induced by heavy weight vehicles have been recorded for more than 5 years. This research shows encouraging results and the potential for the NCR to be used as a simplistic metric for damage detection using FBG strain sensors.

Proceedings Article | 1 April 2016 Paper

A curvature based approach using long-gage fiber optic sensors

Kaitlyn Kliewer, Branko Glisic

Proceedings Volume 9805, 980520 (2016) https://doi.org/10.1117/12.2219323

KEYWORDS: Fiber Bragg gratings, Fiber optics sensors, Sensors, Structural health monitoring, Structural monitoring, Damage detection, Bridges, Statistical analysis

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Fiber Bragg grating (FBG) sensors offer a significant advantage for structural health monitoring due to their ability to simultaneously monitor both static and dynamic strain while being durable, lightweight, capable of multiplexing, and immune to electro-magnetic interference. Drawing upon the benefits of FBG sensors, this research explores the use of a series of long-gage fiber optic sensors for damage detection of a structure through dynamic strain measurements and curvature analysis. Typically structural monitoring relies upon detecting structural changes through frequency and acceleration based analysis. However, curvature and strain based analysis may be a more reliable means for structural monitoring as they show more sensitivity to damage compared to modal parameters such as displacement mode shapes and natural frequency. Additionally, long gage FBG strain sensors offer a promising alternative to traditional dynamic measurement methods as the curvature can be computed directly from the FBG strain measurements without the need for numerical differentiation. Small scale experimental testing was performed using an aluminum beam instrumented with a series of FBG optical fiber sensors. Dynamic strain measurements were obtained as the aluminum beam was subjected to various loading and support conditions. From this, a novel normalized parameter based on the curvature from the dynamic strain measurements has been identified as a potential damage sensitive feature. Theoretical predictions and experimental data were compared and conclusions carried out. The results demonstrated the potential of the novel normalized parameter to facilitate dynamic monitoring at both the local and global scale, thus allowing assessment of the structures health.

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