Quality Function Deployment Based Method for Condition Assessment of Concrete Bridges
Bibliographic record
Abstract
Bridge condition assessment is essential step in bridge management. To ensure safety and serviceability of bridge infrastructure, accurate condition rating methods are needed to provide basis for bridge Maintenance, Repair and Replacement (MR&R) decisions. In Canada and the United States, visual inspection and close-up observation are the common practice to detect surface defects and external flaws. Non-Destructive Testing (NDT) and evaluation technologies are used during visual inspection to reveal subsurface defects. It is paramount to develop systematic methods to capture inspection data and to produce robust condition rating and MR&R decisions. The current research reviews current practice in bridge condition assessment and discusses the main deteriorations and defects identified during visual inspection and NDT and evaluation. Further, the research discusses limitations of available bridge condition assessment models and introduces the Quality Function Deployment (QFD) theory as a novel approach to the area of bridge management. The principles of the QFD theory are demonstrated with a real case study and the potential of using the approach in the area of bridge condition assessment is discussed. Different defects correlations are measured based on an expert’s opinion and are included in the developed QFD method. Wasserman’s normalization technique is embedded in the method to take into account interdependency between the different defects. The QFD method recommendations are compared to other condition assessment methods and validated against twenty bridge projects which were assessed by bridge inspection teams. The validation showed consistent results between inspection teams’ assessments and the QFD method recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".