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Record W146801596

Development of Deterioration Models for Bridge Decks Using System Reliability Analysis

2013· dissertation· en· W146801596 on OpenAlexaboutno aff
Farzad Ghodoosipoor

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2013
Typedissertation
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Bridge (graph theory)Reliability engineeringEngineeringProcess (computing)Structural systemDeckStructural engineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

Generally, in the existing Bridge Management Systems(BMS) deterioration is modeled based on the visual inspections where the corresponding condition states are assigned to individual elements. In this case, the limited attention is given to the correlation between bridge elements from structural perspective. In this process, the impact of the history of deterioration on the reliability of a structure is disregarded which may lead to inappropriate conclusions. The Improved estimate of service life of a bridge deck may help decision makers enhance the intervention planning and optimize the bridge life cycle costs. A reliability-based deterioration model can potentially be an appropriate replacement for the existing procedures.
\nThe objective of this thesis is to evaluate the system reliability of conventional bridges designed based on the existing codes. According to the methodology developed in this thesis, the predicted element-level structural conditions for different time intervals are applied in the non-linear Finite Element model of a bridge superstructure and the system reliability indices are estimated for different time intervals. The resulting degradation curve could be calibrated and updated based on the outcomes of the visual inspections. Also, the reliability of innovative bridges that use non-conventional materials or structural forms such as Steel-Free Deck System has been evaluated by applying the newly developed method. The available deterioration models for conventional superstructuresare not applicable for the innovative bridge systems. Since there is no established deterioration model available for these innovative structures, it is difficult to predict the reliability of such bridges at different time intervals. The method developedhere adopts the reliability theory and establishes deterioration models for conventional and innovative bridges based on their failure mechanisms.
\nThis method has been applied in simply-supported traditional reinforced-concrete bridge superstructures designed according to the Canadian Highway Bridge Design Code (CHBDC-S6), and in an innovative structure with a Steel-Free Deck System, namely the Crowchild Bridge, in Calgary, Canada, as case studies. As an example to show the application of such developed deterioration curve, the developed model has been adopted in an old superstructure in Montreal. The results obtained from the newly developed model and bridge engineering groups’ estimations are found to be in accordance. Based on the reliability estimates, the conventional bridges designed based on the new code are found to be in a good condition during the initial stages of their service life,but their condition degrades faster once corrosion in steel reinforcements is initiated and spalling of concrete becomes evident. In case of the Steel-Free Deck, there is a low probability of failure at the end of the 75 years of its service life. It is found that the element-level assessment of a concrete deck is a conservative approach, since the interaction between the structural elements results in considerably higher reliability index and lower probability of failure. This thesis demonstrates how the proposed system reliability-based evaluation method can be adopted in determining the structural condition of a bridge which represents an important step forward in Bridge Management Systems. The system reliability deterioration model can be easily integrated to the existing Bridge Management Systems (BMS) by replacing the existing condition index by the reliability index or adding it to the assessing process as an additional parameter.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.046
GPT teacher head0.281
Teacher spread0.235 · 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.

Study designBench or experimental
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

Citations4
Published2013
Admission routes1
Has abstractyes

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