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Keywords = Temperature-Driven Structural Health Monitoring (TD-SHM)

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21 pages, 4849 KiB  
Article
Identifying Time Periods of Minimal Thermal Gradient for Temperature-Driven Structural Health Monitoring
by John Reilly and Branko Glisic
Sensors 2018, 18(3), 734; https://doi.org/10.3390/s18030734 - 1 Mar 2018
Cited by 22 | Viewed by 4813
Abstract
Temperature changes play a large role in the day to day structural behavior of structures, but a smaller direct role in most contemporary Structural Health Monitoring (SHM) analyses. Temperature-Driven SHM will consider temperature as the principal driving force in SHM, relating a measurable [...] Read more.
Temperature changes play a large role in the day to day structural behavior of structures, but a smaller direct role in most contemporary Structural Health Monitoring (SHM) analyses. Temperature-Driven SHM will consider temperature as the principal driving force in SHM, relating a measurable input temperature to measurable output generalized strain (strain, curvature, etc.) and generalized displacement (deflection, rotation, etc.) to create three-dimensional signatures descriptive of the structural behavior. Identifying time periods of minimal thermal gradient provides the foundation for the formulation of the temperature–deformation–displacement model. Thermal gradients in a structure can cause curvature in multiple directions, as well as non-linear strain and stress distributions within the cross-sections, which significantly complicates data analysis and interpretation, distorts the signatures, and may lead to unreliable conclusions regarding structural behavior and condition. These adverse effects can be minimized if the signatures are evaluated at times when thermal gradients in the structure are minimal. This paper proposes two classes of methods based on the following two metrics: (i) the range of raw temperatures on the structure, and (ii) the distribution of the local thermal gradients, for identifying time periods of minimal thermal gradient on a structure with the ability to vary the tolerance of acceptable thermal gradients. The methods are tested and validated with data collected from the Streicker Bridge on campus at Princeton University. Full article
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
Show Figures

Figure 1

Figure 1
<p>Example of a beam-like bridge instrumented with a chain of parallel sensors along the deck and displacement sensors at extremity (Streicker Bridge).</p>
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<p>Example of a bridge cross-section with linear temperature distribution.</p>
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<p>Example of a bridge cross-section with non-linear temperature distribution (causing non-linear thermal gradient).</p>
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<p>Example of a 3D signature of temperature, strain, and longitudinal displacement with best fit line, taking into account data resulting from non-linear temperature distributions.</p>
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<p>2D projections of 3D signature, keeping temperature as the vertical axis.</p>
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<p>Example of temperature variation over two days in two cross-sections of Streicker Bridge (top and bottom sensors in cross-sections at P11 and P12).</p>
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<p>Flowchart describing process of using methods for identifying time periods of minimal thermal gradient.</p>
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<p>Range of temperature measurements from spring 2016.</p>
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<p>Points considered as having minimal gradients, observed using MR methods.</p>
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<p>3D signature example with best fit line (BFL) for time points with minimal thermal gradient, identified using MR method.</p>
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<p>2D projections of 3D signature, keeping temperature as the vertical axis. Red indicates time points of minimal thermal gradient found using MR = 4 °C/m, and red line indicates best fit line for minimal gradient data set, as in <a href="#sensors-18-00734-f010" class="html-fig">Figure 10</a>.</p>
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<p>Maximum vs. mean absolute local thermal gradient.</p>
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<p>3D signature with best fit line (BFL) for time points with minimal thermal gradient, identified using (Mean Local Gradient) MeLG method.</p>
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<p>2D projections of 3D signature which significantly reduce the bi-linearity. Red indicates time points of minimal thermal gradient found using MeLG = 4.5 °C/m, and red line indicates best fit line for minimal gradient data set, as in <a href="#sensors-18-00734-f013" class="html-fig">Figure 13</a>.</p>
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<p>MR and MeLG Comparison, Pier 12.</p>
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<p>Summer 2010 Streicker Bridge minimal thermal gradient methods comparison.</p>
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