Bridge service life and impact of maintenance events on the structural state index
Bibliographic record
Abstract
Managers of ageing structures aim to extend their service life through timely inspections and preventive maintenance. Currently, planning and decisions on maintenance activities are based on condition assessment methods that rely primarily on visual inspections. However, experience indicates that the deterioration is already in an advanced state at the appearance of the first visual signs of distress and precludes timely preventive maintenance interventions. In addition, the analysis of a sequence of ratings solely based on the evolution of visually based ratings provides very little insight on the rate of the deterioration, and the estimation of residual service life. The objective of this paper is to propose a procedure that supplements visual inspection reports based on structure specific nonlinear simulations of deteriorations associated with the ingress of chloride ions, corrosion of the reinforcing steel, and the cracking and spalling of concrete. Structure-specific simulations are obtained though a model that uses site-specific hourly historical meteorological data, and concrete properties from non-destructive permeability and resistivity measurements. The model is used to estimate the time to the initiation of corrosion as well as the time to the first corrosion-induced cracks to anticipate and evaluate pre-visual conditions. The proposed methodology is demonstrated for an ageing structure in Montreal (Canada). The results show that visual inspections fail in detecting the onset of corrosion for preventive maintenance, and result in prematurely deficient structures. Model simulations suggest that preventive maintenance on the bridge at year 20, corresponding to the bifurcation point of accelerated deterioration rates, would have been optimal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".