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Enregistrement W7033712510

Seismic Fragility Assessment of As-built and Retrofitted Bridges using Fiber Reinforced Elastomeric Isolator

2022· dissertation· en· W7033712510 sur OpenAlexaboutno aff

Notice bibliographique

RevueMacSphere (McMaster University) · 2022
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueDiverse Scientific and Economic Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFragilityBridge (graph theory)IsolatorProbabilistic logicSeismic riskVulnerability assessmentStiffness
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Highway bridges are considered to be one of the most susceptible constituents of transportation networks when they are subjected to severe natural hazards such as earthquakes and environmental exposures like subfreezing temperatures. To facilitate and enhance pre-hazard event mitigation and post-hazard emergency response strategies, probabilistic risk assessment methodologies have attracted increased attention, recently. Seismic fragility assessment is one of the probabilistic techniques which predicts the damage risk of the structure for a given hazard level. While fragility curves can be developed using different methods, such as expert-based, empirical, experimental, analytical, and hybrid, analytical fragility curves are perceived to be the most reliable and least biased technique. Seismic isolation systems are prevalently used in bridge structures to mitigate the damage risk of bridge components against natural hazards. However, the effectiveness of implementing recently emerged isolators such as Stable Unbonded Fiber Reinforced Elastomeric Isolators (SU-FREI) should be examined by developing analytical fragility curves of retrofitted bridges and quantifying the mitigation in the damage probability of different bridge components. In this regard, incorporating the Soil-Structure Interaction (SSI) is critical since the lateral response of bridges relies on the relative stiffness of bridge components, such as columns and isolators and the supporting soil. In addition, all bridge components are exposed to environmental stressors like subfreezing temperature that can alter the seismic response of bridges. In the first phase of this thesis, a seismic fragility assessment is carried out on an existing multi-span continuous reinforced concrete bridge. Two bridge representations are developed to simulate the as-built bridge along with its retrofitted counterpart utilizing SU-FREI. An Incremental Dynamic Analysis (IDA) is conducted using 45 synthetic ground motion records developed for eastern Canada and damage limit states are applied to generate fragility curves and determine the probability of damage to different bridge components. Bridges are analyzed in longitudinal and transverse directions, independently, and component- and system-level fragility curves are developed. In the second phase, the previously generated bridge models are expanded to incorporate the SSI effects by introducing the pile groups under piers and abutments. Several interactions including deck-abutment, abutment-embankment, pile-soil, and pile-soil-pile interactions are considered. A significant challenge in this phase is the accurate simulation of the lateral and vertical behavior of pile groups since all pile groups comprised of closely-spaced vertical and battered piles. A ground motion suite consisting of 45 ground motions has been selected, which reflects the seismicity of the bridge site. IDA is conducted to monitor the seismic performance of the bridge from the elastic linear region up to collapse. Fragility curves, which serve as an important decision-support tool have been developed to identify the potential seismic risk of the bridge. In the third phase, a multi-hazard assessment is carried out by conditioning the previously developed bridge models (i.e. monolithic fixed-base, isolated fixed-base, monolithic with SSI, and isolated with SSI) to a range of room and subfreezing temperatures and applying a seismic excitation, simultaneously. The cold temperature behavior of the constitutive materials of different bridge components, namely, concrete, reinforcing steel, rubber, and the supporting soil are studied and reflected in the bridge models. IDA is performed and damage potential of different bridge components are quantified. In summary, it is demonstrated that SU-FREI is a competing alternative for seismic isolation of bridges by offering potentially less manufacturing time and cost, lower weight, and easier installation which is an attractive feature for accelerated bridge construction applications. In all three phases, it is shown that the bridges which are isolated using SU-FREI have improved seismic performance in comparison with monolithic bridges by exhibiting lower probability of damage to the primary bridge components like columns and pile caps and transferring the damage to less important components such as abutments at which damage does not cause bridge closure. In addition, it is shown that seismic isolation using SU-FREI can effectively mitigate the seismic demand and damage potential of the constitutive components of a bridge supported by weak soil. While occurrence of seismic events along with an environmental stressor such as cold temperature can drastically jeopardize the functionality of a bridge supported by weak soil, it is demonstrated that seismic isolation using SU-FREI can significantly alleviate the probability of damage to bridge components.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,228
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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