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Record W2524953848

Evaluating the Retrofit of Highway Bridges Using Fluid Viscous Dampers

2012· dissertation· en· W2524953848 on OpenAlexaboutno aff
Asim Rustum

Bibliographic record

VenueUWSpace (University of Waterloo) · 2012
Typedissertation
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDamperStructural engineeringEngineeringGeotechnical engineeringCivil engineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

Highway bridges function as the arteries of our society. Hence, it is essential that they remain operational following an earthquake. Unfortunately, a significant number of bridges worldwide, including in Canada, were constructed prior to the development of modern seismic design provisions. In many cases, such bridges are expected to perform poorly during earthquakes. According to a report published in 2000 by Ministry of Transportation of Ontario (MTO), in eastern Ontario alone, there are over 70 bridges that are structurally deficient. Current methods to retrofit these bridges to bring them into compliance with the existing codes would entail substantial structural modifications. Examples of such modifications include the replacement of existing rocker bearings with elastomeric bearings, structural strengthening of piers, and enlarging the bearing surfaces. These methods involve substantial cost, effort, and materials. \n \nAn alternative means to retrofit structurally deficient bridges is investigated in this thesis. This method involves using a combination of elastomeric bearings and fluid dampers to retrofit highway bridges. In principle, these devices work in the same way as shock absorbers in automobiles. They absorb shock and dissipate the vibration energy to the environment as heat. In the case of bridges, earthquakes impart the shock to the structure. Before these devices can be implemented in practice, there are many issues that need to be understood with respect to their performance and modelling. Moreover, a comparative assessment between popular retrofit options employing isolation systems needs to be undertaken to verify and provide a benchmark to assess their performance. \n \nThe Mississippi River Bridge near Ottawa is chosen as a test structure to conduct this study. This bridge already contains an advanced isolation system, and has an extensive documentation available for modelling and verification. Various retrofit options will be studied and compared with the existing isolation design for this bridge. In all cases, the effect of soil-structure interaction is included. A comprehensive set of performance indices are used to evaluate the performance of various retrofit options. All the models are constructed in the open source software, OpenSees. \n \nThe research demonstrates that the proposed approach is a viable retrofit method for highway bridges. Moreover, compared to advanced isolation systems, retrofit using elastomeric bearings with viscous dampers was successful on transferring lower loads to the substructure, and resulted in lower superstructure displacements. Though this study involved one bridge, it has provided a computational test bed to perform further studies and has provided valuable insight into the modeling and performance of retrofit solutions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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 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
Published2012
Admission routes1
Has abstractyes

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