Thesis (M.S.)--University of North Texas, Dec., 2008. / Title from title page display. Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin--Madison, 1982. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 75-77).

Thermography is one technology, which can be used to detect thermally significant defects in buildings and is traditionally performed using a walk-through methodology. Yet because of limitations such as transient climatic changes, there is a key performance gap between image capture and interpretation. There are however new methodologies currently available, which actively address some of these limitations. By better understanding alternative methodologies, the performance gap can be reduced. This thesis contrasts three thermography methodologies (Walk-through, time-lapse and pass-by) to learn how they deal with limitations and address specific building defects and thermal performance issues. For each approach, practical methodologies were developed and used on laboratory experiments (hot plate) and real dwelling case studies. For the real building studies, 133 dwellings located in Devon and Cornwall (South West England) were studied; this sample represents a broad spectrum of construction types and building ages. Experiments testing these three methodologies found individual strengths and weaknesses for each approach. Whilst traditional thermography can detect multiple defects, characterisation is not always easy to achieve due to the effects of transient changes, which are largely ignored under this methodology. Time-lapse thermography allows the observation of transient changes from which more accurate assessment of defect behaviour can be gained. This is due to improved differentiation between environmental conditions (such as cloud cover and clear sky reflections), actual material thermal behaviour and construction defects. However time-lapse thermography is slow, complex and normally only observes one view. Walk-past thermography is a much faster methodology, inspecting up to 50 dwellings per survey session. Yet this methodology misses many potential defects due to low spatial resolutions, single (external only) elevation inspection and ignoring transient climate and material changes. The implications of these results for building surveying practice clearly indicate that for an improved defect characterisation of difficult to interpret defects such as moisture ingress, thermographers should make use of time-lapse thermography. A review of methodology practicalities illustrates how the need for improved characterisation can be balanced against time and resources when deciding upon the most suitable approach. In order to help building managers and thermographers to decide on the most suitable thermography approach, two strategies have been developed. The first combines different thermography methodologies into a phased inspection program, where spatial and temporal resolution increase with each subsequent thermography inspection. The second provides a decision-making framework to help select the most appropriate thermography methodology for a given scenario or defect.

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This research focuses on characterizing a potato with extra sugar content and identifying the location and depth of the extra sugar content using the active thermography imaging technique. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The inspection system and general methodologies characterizing extra sugar content will be presented in this study. The average heating rate obtained from the thermal image analysis is the major factor in characterization procedures. Using information on the average heating rate, the probability of achieving a potato with extra sugar content may be predicted using the logistic regression model. In addition, neural networks are also used to identify the potato with extra sugar contents. The correct rate for identifying a potato with extra sugar content in it can reach 85%. The location of extra sugar content can also be found using the logistic regression model. Results show the overall correct rate predicting the extra sugar content location with a resolution of 20 by 20 pixels is 91%. In predicting the extra sugar content depth, amounts exceeds 2/3 inches are not detectable by analyzing thermal images. The depth of extra sugar content can be discriminated in 0.3 inch increments with a high rate of accuracy (87.5%).

Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2004. / Dr. Zureick, Abdul-Hamid, Committee Member; Dr. White, Donald, Committee Member; Dr. Saxena, Ashok, Committee Member; Dr. Jacobs, Laurence, Committee Member; Dr. Haj-Ali, Rami, Committee Chair; Dr. Armanios, Erian, Committee Member. Vita. Includes bibliographical references (leaves 164-172).

Thesis (M.S.)--Rochester Institute of Technology, 1992. / Typescript. Includes bibliographical references (leaves 53-56).

Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references (leaves [74-77]).