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Enregistrement W7071256186

Seismic Performance Evaluation of Industrial and Nuclear Reinforced Concrete Shear Walls: Hybrid Simulation Tests and Data-Driven Models

2024· dissertation· en· W7071256186 sur OpenAlexfundno aff

Notice bibliographique

RevueMacSphere (McMaster University) · 2024
Typedissertation
Langueen
DomaineEngineering
ThématiqueSeismic Performance and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaFederal Emergency Management AgencyNational Institute of Standards and TechnologyMcMaster University
Mots-clésReinforced concreteShear wallSeismic analysisSeismic riskSeismic retrofitShear (geology)Bridge (graph theory)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Low-aspect-ratio reinforced concrete (RC) shear walls, characterized by height-to-length ratios of less than two, have been widely used as a seismic force-resisting system (SFRS) in a wide array of structures, ranging from conventional buildings to critical infrastructure systems such as nuclear facilities. Despite their extensive applications, recent research has brought to light the inadequate understanding of their seismic performance, primarily attributed to the intricate nonlinear flexure-shear interaction behaviour unique to these walls. In this respect, the current research dissertation aims to bridge this knowledge gap by conducting a comprehensive evaluation to quantify the seismic performance of low-aspect-ratio RC shear walls when used in different applications. Chapter 2 focuses on low-aspect-ratio RC shear walls that are employed in residential and industrial structures. Considering their significance, the seismic response modification factors of such walls, as defined in various standards, are thoroughly examined and evaluated utilizing the FEMA P695 methodology. The analysis revealed potential deficiencies in the current code-based recommendations for response modification factors. Consequently, a novel set of response modification factors, capable of mitigating the seismic risk of collapse under the maximum considered earthquake, is proposed. Such proposed values can be integrated into the forthcoming revisions of relevant building codes and design standards. While the FEMA P695 methodology offers a comprehensive approach to assessing building seismic performance factors, its practical implementation is associated with many challenges for practicing engineers. Specifically, the methodology heavily relies on resource-intensive and time-consuming incremental dynamic analyses, making it less feasible for routine engineering practices. To enhance its practicality, a data-driven framework is developed in Chapter 3, circumventing the need for such demanding analyses. This framework provides genetic programming-based expressions capable of producing accurate predictions of the median collapse intensities—a key metric in the acceptance criteria of the FEMA P695 methodology, for different structural systems. To demonstrate its use, the developed framework is operationalized to low-aspect-ratio RC shear walls and the predictive expression is evaluated considering several statistical and structural parameters, which showed its adequacy in predicting the median collapse intensities of such walls. Furthermore, the adaptability of this framework is showcased, highlighting its applicability across various SFRSs. Chapters 4 and 5 tackle the scarcity of experimental assessments pertaining to the seismic performance of low-aspect-ratio RC walls in nuclear facilities. The seismic hybrid simulation testing technique is employed herein to merge the simplicity of numerical simulations with the efficiency of experimental tests. Hybrid simulation can overcome obstacles related to physical specimen sizes, limited actuator capacities, and space constraints in most laboratories. In these two chapters, the experimental program delves into evaluating the seismic performance of three two-storey low-aspect-ratio nuclear RC walls under different earthquake levels, including operational, design, and beyond-design-level scenarios. Diverse design configurations, including the use of increased thickness boundary elements and different materials (i.e., normal- and high-strength reinforcement), are considered in such walls to provide a comprehensive understanding of several structural parameters and economic metrics. Key structural parameters, such as the force-displacement responses, multi-storey effects, lateral and rotational stiffnesses, ductility capacities, displacement components, rebar strains, crack patterns and damage sequences, are all investigated to provide direct comparisons between the walls in terms of their seismic performances. Additionally, economic metrics, including the total rebar weights, overall construction costs and the expected seismic repair costs, are considered in order to evaluate the seismic performance of the walls considering an economic perspective. The findings of this experimental investigation are expected to inform future nuclear design standards by enhancing the resilience and safety of their structures incorporating low-aspect-ratio RC shear walls.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,036
Tête enseignante GPT0,239
Écart entre enseignants0,203 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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