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Record W1837594448 · doi:10.1520/stp11447s

Pullout Resistance of Roofing Fasteners Using Different Methods

2003· book-chapter· en· W1837594448 on OpenAlexafffund
Angathevar Baskaran, M Sexton, Sudhakar Molleti

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of OttawaNational Research Council Canada
FundersMinistère de la Défense NationalePublic Works and Government Services Canada
KeywordsResistance (ecology)Structural engineeringEngineeringForensic engineeringArchitectural engineeringBiologyEcology

Abstract

fetched live from OpenAlex

Wind uplift rating is one of the key performance requirements for single-ply mechanically attached systems. It depends on the properties of the membrane (mechanical, physical and chemical), substrate (compressive strength and dimensional stability), and deck (thickness and deflection). An attachment system (fastener, plate and seam) integrates the above components to form an assembly. Fastener pullout resistance (FPR) from the deck is one of the essential design parameters in the system specification. As well, the FPR is an indicator of the existing deck condition in reroofing/recover applications. In the field, the FPR values are obtained using pullout testers. To quantify the accuracy of the manual pullout testers under various environmental conditions, an experimental program is in progress at the National Research Council's Dynamic Roofing Facility (DRF). From this ongoing research, this paper compares the FPR data from the field pullout tester with those obtained using the laboratory universal Instron testing machine. In the manual pullout tester, consistency of the FPR data depends on the operator. Considering this variable and to generalize the data, the above experiments are performed on two decks (steel and wood) with three different fastener types. The study finds that the field manual pullout tester underestimates the FPR data in comparison to the data obtained from the laboratory machine. This trend is found true irrespective of the selected decks and fasteners. In the roofing market, automatic pullout testers are also available that function using a battery-powered source. Selected experiments were performed to identify the influence of the pull out speed on the FPR data using an automatic pullout tester.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.480
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations4
Published2003
Admission routes2
Has abstractyes

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