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Record W2800936119 · doi:10.22215/etd/2015-10893

Modeling of Barrier Failure and Fire Spread in CUrisk

2015· dissertation· en· W2800936119 on OpenAlexaboutno aff
Xiao Li

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsFire hazardEngineeringProbabilistic logicFire safetyHazardFire protectionForensic engineeringEnvironmental scienceComputer scienceCivil engineering

Abstract

fetched live from OpenAlex

CUrisk, a computer fire risk analysis model, is being developed at Carleton University for over a decade. To better evaluate failure of building elements and spread of fire beyond the room of fire origin, this thesis developed and integrated into CUrisk a barrier failure model and a fire spread model. A probabilistic fire spread model developed at Carleton University was incorporated into CUrisk system model. The role and position of the Fire Spread submodel were analyzed and changes to the system model and some other submodels were undertaken. With these modifications, CUrisk can employ the Fire Spread submodel to predict the fire hazard conditions in a building fire, and to use the results to predict the life risk and fire damages. Through a comprehensive case study the Fire Spread submodel demonstrated good results. To assist the development of a barrier failure model for CUrisk, six full-scale room fire tests were conducted and analyzed. The fire evolution and contribution of the timber assembly components to the room fire were investigated. The response of assembly components in the fire were studied as well. The test findings were used to improve the CUrisk submodels and particularly to develop the barrier failure model. A barrier failure model was developed based on the concept of component subtractive method. The temperature profile of a building assembly is calculated by a one-dimensional finite difference heat transfer approach. A probabilistic barrier failure model was developed by taking into account the uncertainties of some factors that affect the assembly failure. With this model, the probability of failure as a function of fire exposure time can be generated. The model performance was verified by comparing with the fire test measurements, which demonstrated good agreements. Comparable results are also predicted regarding the fall-off behaviour of the fire-exposed gypsum board as well as the charring behaviour. Finally, a fire risk analysis case study was conducted on a six-storey apartment building. Results indicated that CUrisk can better handle the impact of fire barriers on the fire risk with the new Barrie Failure submodel.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.470

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)0.0000.000

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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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
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
Published2015
Admission routes1
Has abstractyes

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