Fuzzy logic-visual inspection based evaluation charts for lateral capacity prediction of deteriorating concrete piled wharves
Bibliographic record
Abstract
Systematic and quantitative assessment of the lateral load capacity of deteriorating wharves is vital for ensuring structural integrity and planning repairs. While various assessment methods exist, such as structural health monitoring and reliability analysis, they can be costly for large infrastructure populations. Instead, visual inspections (VI) remain the primary assessment method despite its qualitative nature, inducing uncertainty in capacity evaluation. Thus, a new fuzzy-logic framework is proposed to translate VI data, such as linguistic defect descriptions, into quantitative capacity estimates, with the ability to update these estimates upon availability of field test data. The framework uses fuzzy deterioration models to map observed defects into a min-max range of material property reductions, inputted into a finite element model of a class-representative wharf to estimate the degraded capacity. Hence, enabling the calibration of wharf-class specific capacity-defect chart. A wharf condition index (WCI) is proposed to score the observed defects on a scale of 0–100. By inputting WCI into the chart, a best- and worst-case capacity can be estimated. The framework application is showcased for reinforced concrete wharves. Results show that the WCI can be related to the capacity via two-term exponential models with R 2 of 86.3% to 94.2% for the best- and worst-case models, respectively. The proposed capacity-defect chart was validated by predicting the capacities of five scaled corroded piles, from a past experimental study, based on the observed corrosion-induced cracks. The capacities of four out of five piles were within the predicted range with one falling marginally outside the predicted range by about 4%. • Fuzzy logic approach for the capacity prediction of wharves based on visual inspection. • Proposal of a new wharf condition index for condition rating of in-service wharves. • Developing charts to map the wharf condition index into min-max range of capacities. • Chart practicality was validated using a past experimental study on corroded wharf.
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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".