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Record W3180483869 · doi:10.20381/ruor-26280

Performance Requirements for Climate Resilience of Residential Roofs

2021· dissertation· en· W3180483869 on OpenAlexfundvenueaboutno aff
Flonja Shyti

Bibliographic record

VenueNPARC · 2021
Typedissertation
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsResilience (materials science)Environmental scienceArchitectural engineeringEngineeringEnvironmental resource managementEnvironmental planningMaterials science

Abstract

fetched live from OpenAlex

The roof is one of the most important elements of the building envelope. It offers protection for the homeowners against weather elements. According to the Institute for Catastrophic Loss Reduction (ICLR) houses are less engineered and are much more prone to damages during weather events. Recent climatic changes have increased the severity of weather events and roof failures. This demands the necessity of updating the design, evaluation, and construction methods for asphalt shingle roofs with a holistic approach. A disconnect exists between the service life of a roof and the advertised warranties of asphalt shingles by the manufactures. This is misleading and prevents homeowners from making informed decisions about their roof selection. This thesis reviewed not only the roof's performance in the field but also the current practices followed for the design, evaluation, and construction methods. The review respectively concluded that the current design is based on only historical data, both components and systems are mostly evaluated under lab conditions, rather than being exposed to the weather conditions experienced during the service life, and lack of installation quality assurance. The objective of this thesis is to develop the initial framework for a “Holistic Approach” that would increase the resilience of residential roofs. This has been accomplished in three folds: 1. Quantified the changes in the design wind loads, using both the current and projected changes in wind pressures due to global warming magnitude. Data provided by Environment and Climate Change Canada was used for this analysis and a new web design tool “Climate-RCI” for climate severity classification of cities across Canada was created. 2. Performed an extensive experimental evaluation of components and systems, following established methods and procedures, to quantify the effect of weathering on the resistance of asphalt shingles and roof mock-ups. To achieve this both components and systems were evaluated. For the component, the resistance after being aged in the field for a minimum of 13 years displayed a maximum decrease of 59%. Measured properties of the majority of the aged components no longer met the minimum requirements outlined in the respective standards referenced in the National Building Code of Canada. The system resistance was significantly reduced due to weather exposure. 3. Developed quality assurance guidelines based on best practices in consultation with the roofing industry for various climatic severities. This thesis identified three major future research areas, namely, calibration of an appropriate resistance factor for residential roofs, correlating lab verse field weather exposure conditions, and probabilistic determination of the installation process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.242
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes3
Has abstractyes

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