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Enregistrement W7001228562

Improvement of the approaches to monitoring of reinforced concrete bridges subjected to chloride-induced reinforcement corrosion in bridge management systems

2023· dissertation· en· W7001228562 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen University of Cape Town (University of Cape Town) · 2023
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant chemical constituents analysis
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of NamibiaFP7 International CooperationDeutsche Gesellschaft für Internationale ZusammenarbeitUniversity of Cape Town
Mots-clésServiceability (structure)Bridge (graph theory)Reinforced concreteDurabilityRebarStructural health monitoringCorrosionVisual inspection
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Reinforcement corrosion in reinforced concrete (RC) bridges has undeniably become the main cause of civil infrastructure durability problems in many countries. The steel reinforcing in concrete corrode when certain conditions (e.g., carbonation, chlorides, insufficient cover and moisture) are met. Even though RC bridges are built to provide a service over a specified period, their serviceability changes over time due to gradual deterioration. In the marine environment, this deterioration is mainly caused by the ingress of chlorides in concrete, which results in chlorideinduced steel corrosion. Thus, effective maintenance and management are needed to ensure the functionality of bridges throughout their designed service life. Bridge management systems (BMSs) are effective means for managing bridges throughout their design life. BMSs require the collection of data related to the condition of the bridge for decision making. South Africa uses the Struman BMS to assess and prioritise bridges for maintenance and repair. The system solely relies on visual inspection as a basis for the identification of deterioration processes and bridge condition rating. However, visual inspections do not allow detection of rebar corrosion until damage has occurred; and once the damage has occurred, it becomes very costly to repair. Thus, this research focuses on identifying available reinforcement corrosion monitoring methods that can be used for condition assessment to supplement visual inspections in the Struman BMS. The research involved reviewing different BMSs with the emphasis on their structure, application, and assessment methodology. The review of various BMSs used in the USA, Canada, UK and Southern Africa shows that while BMSs differ, they all assess the risk of failure and prioritise bridges for repair within limited budgets. Focusing on the Struman BMS, shortcomings with respect to the monitoring of reinforcement corrosion were identified. It was found that the condition ratings of RC bridges are based on visible defects assessed during visual inspections, which is still the dominant method used for bridge inspection. There exists a lack of integration of appropriate monitoring systems in the BMS, especially with regards to the monitoring of reinforcement corrosion. Available corrosion monitoring methods were critically reviewed based on their principles, applications, and technical aspects. These corrosion monitoring methods were categorised into visual inspections, Non-Destructive Testing (NDT), and remote monitoring methods. It became evident that monitoring technologies are continuously evolving and that the progress achieved to date is promising. However, further development is needed when using these methods in the BMSs for condition assessment, particularly in the continuous monitoring of bridges. The use of these modern technologies will allow earlier detection of problems and hence support sound maintenance and damage prevention. This is expected to simultaneously reduce the costs needed for maintenance and repair. In addition, modern monitoring methods have the potential to enhance the speed and scope of condition assessments, to provide reliable and wide-ranging data, and to reduce traffic interruptions when taking measurements. An integrated approach is proposed based on applying corrosion monitoring methods as a supplement to visual inspections in the Struman BMS. Integrating these methods with the existing approach to bridge management is expected to compensate for the limitations of the Struman BMS and enhance its capability. The proposed approach involves identifying bridges that need to be assessed (new or existing bridges). Selecting monitoring methods for new bridges based on the needs and the type of data that need to be collected from bridges, and existing bridges based on the past assessment data and visual inspection. Monitoring is proposed to incorporate periodic and permanent monitoring. The former includes NDT measurements using any selected methods, and the latter includes installing sensor systems in new and existing bridges. Condition ratings are defined for monitored parameters, including corrosion risk (from corrosion potentials and electrical resistivity), corrosion rate, pulse velocity, defective areas (from the presence of cracks and delamination), moisture content and visual inspection. Other corrosion-related parameters considered include chloride content, cover depth measurement and monitoring time to corrosion onset. The obtained data from visual inspection and corrosion monitoring methods are then holistically analysed and interpreted to obtain integrated condition ratings, which are used to rate the condition of the bridge and its various RC elements. Consequently, this approach is expected to assist in prioritising critical bridges for repair and maintenance. Lastly, recommendations are provided related to a comprehensive study on the implementation of available reinforcement corrosion monitoring methods for the monitoring of in-service bridges. The findings of this research also need to be taken forward by an experienced bridge inspector to help develop practical guidelines for applying corrosion monitoring methods in the Struman BMS.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,520
Score d'incertitude au seuil0,958

Scores Codex et Gemma par catégorie

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

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 Opus0,055
Tête enseignante GPT0,214
Écart entre enseignants0,159 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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