Infrared Thermography Sample Clauses

Infrared Thermography. Testing agency shall survey entire roof area using infrared color thermography according to ASTM C1153.
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Infrared Thermography infrared imaging may be used to identify areas of air leakage and gaps in the thermal envelope. Except at the roof, effective testing requires a minimum of 18⁰F of temperature difference between the inside and outside of the building and should be performed after sundown (particularly on the roof) or before sunup to minimize solar influence. It is preferable to obtain infrared images from the interior of the wall assembly, since most wall systems are built with cavities between the veneer and the structural wall, where the air barrier plane is located. Infrared images of the exterior wall surface will often show temperature anomalies at weeps and other designed openings in the veneer system. To perform thermography, the building is placed under negative or positive pressure using the building’s HVAC system. Infrared testing is usually performed concurrently with the Air Infiltration testing described above, to make use of the air pressure differential created during the ASTM E779 test.
Infrared Thermography. Infrared thermography (IRT) is used to produce an image of the thermal energy emitted from the surface of a material. The emitted energy is typically presented in the image as surface temperature. Anomalies in the surface temperature are assessed to identify subsurface defects such as voids or delamination, or surface defects such as cracks or spalls. The defects alter the local heat transfer properties of the material, producing an anomaly in the surface temperature at the location of the defect. The contrast in surface temperature between areas where there is a defect and intact areas of material facilitates the detection of the defects. Active or passive heating may be used to induce heat transfer to produce the anomalies in surface temperature. Active heating consists of applying heat to the surface of the material in the area to be imaged with IRT and capturing images during the heating or cooling of the surface. Passive heating typically consist of diurnal weather patterns that result in heating during the daytime and cooling at night. IRT has been successfully used for the detection and measurement of surface and subsurface defects in reinforced concrete structures, including cracking and delamination. However, the depth and thickness of the subsurface defect is not easily determined without supporting experimental or numerical calibration data. Conventional IRT technologies are generally ineffective in identifying or quantifying grout and strand conditions for embedded post-tensioning steel ducts, primarily because of the cover depth and relatively small dimensions of the duct.

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  • Bibliography [Ben83] Xxxxxxx Xxx-Or. Another advantage of free choice (extended ab- stract): Completely asynchronous agreement protocols. In Proceed- ings of the second annual ACM symposium on Principles of distrib- uted computing, pages 27–30. ACM, 1983. [BG89] Xxxxx Xxxxxx and Xxxx X Xxxxx. Asymptotically optimal distributed consensus. Springer, 1989. [BGP89] Xxxxx Xxxxxx, Xxxx X. Xxxxx, and Xxxxxxx X. Xxxxx. Towards optimal distributed consensus (extended abstract). In 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, North Carolina, USA, 30 October - 1 November 1989, pages 410–415, 1989. [BT85] Xxxxxxx Xxxxxx and Xxx Xxxxx. Asynchronous consensus and broadcast protocols. Journal of the ACM (JACM), 32(4):824–840, 1985. [DGM+11] Xxxxxxxx Xxxxx, Xxxxxx Xxx Xxxxxxxx, Xxxxxx Xxxxxx, Xxxxxx Xxxxxxxxx, and Xxxxxxxxx Xxxxxxxxxx. Stabilizing Consensus with the Power of Two Choices. In Proceedings of the Twenty-third Annual ACM Symposium on Parallelism in Algorithms and Architectures, SPAA, June 2011. [DS83] Xxxxx Xxxxx and X. Xxxxxxx Xxxxxx. Authenticated algorithms for byzantine agreement. SIAM Journal on Computing, 12(4):656–666, 1983. [FG03] Xxxxxxxx Xxxxx and Xxxx X Xxxxx. Efficient player-optimal protocols for strong and differential consensus. In Proceedings of the twenty- second annual symposium on Principles of distributed computing, pages 211–220. ACM, 2003.

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