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Record 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 on OpenAlexaboutno aff

Bibliographic record

VenueOpen University of Cape Town (University of Cape Town) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPlant chemical constituents analysis
Canadian institutionsnot available
FundersUniversity of NamibiaFP7 International CooperationDeutsche Gesellschaft für Internationale ZusammenarbeitUniversity of Cape Town
KeywordsServiceability (structure)Bridge (graph theory)Reinforced concreteDurabilityRebarStructural health monitoringCorrosionVisual inspection
DOInot available

Abstract

fetched live from 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.055
GPT teacher head0.214
Teacher spread0.159 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes1
Has abstractyes

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