Date of Award
Doctor of Philosophy
Dr. Garrett O'Sullivan
Mr. Paul O'Sullivan
Understanding the buildings thermal envelope is paramount to analysing existing problems which facilitate cold bridging and low surface temperatures. As no detailed housing stock database is in existence in Ireland which describes the geometrical configuration of the existing housing stock, and to create one would be a major challenge, this research set out to develop a high speed remote cataloguing and geometrical data extraction methodology to build such a database. Replacing traditional measurement methods with a remote method allows for the rapid extraction of fagade information. A remote surveying process is proposed named, the Virtual Survey Method, for the classification of the existing housing stock to provide a foundation platform of information. Building on this platform a Remote Cataloguing, Measurement and Mapping method (RCMM) is proposed, to extract detailed geometrical information from each terrace typology. Terrace facades are then vectorised to map the locations of a defined set of construction details according to envelope category, elevation and orientation interfaces, providing a simplified detail quantification methodology for use in energy efficient retrofit decision support. The RCMM uses photogrammetric techniques for measurement extraction, through the use of static panoramic viewports from Google Street View© and aerial spot images from Google Earth©. This approach speeds up the survey process and generates a classification of the housing stock, and geometrical databases remotely.
This is the first study to develop house archetypes using a fully remote process to characterise the case study housing stock. The terraced housing stock from 1930 to 1982 can be characterised by five detailed house archetypes. This study is also the first study to include a detailed bottom-up level of geometrical characteristics, capable of pin pointing energy loss to specific thermal bridging locations. The proposed remote methodology can be used to develop a highly detailed national database which would reduce uncertainty in the level of accuracy in housing databases. The advantage to using the proposed method over a traditional method is time and expense with the proposed method being up to four times faster than using a traditional method.
Pittam, James, "Thermal Envelope Analysis of Local Authority Low Rise Housing Based on Stock Aggregation Theory Using a Remote Feature Extraction Technique." (2016). Theses [online].
Available at: https://sword.cit.ie/allthe/275