A Quality Function Deployment (QFD) Approach for Bridge Maintenance Management
Bibliographic record
Abstract
Infrastructure age in the US/Canada are beyond half their expected service life. With billions of dollars invested annually, an increase in number of decisions towards maintenance, rehabilitation and replacement (MRR) activities are expected. Customer (infrastructure user) opinions are sometimes sought when major infrastructure-related decisions are made by conducting surveys, community meetings, etc. However, with consumers becoming more involved in economic, environmental, and social issues related to infrastructure, a process for ensuring customer demands are addressed would be valuable to all stakeholders involved. In this thesis, using bridge as an example, an innovative expert-based decision-framework has been proposed and developed using the Quality Function Deployment (QFD) approach. The framework comprises of three major elements. First a hierarchical evidential reasoning (HER) framework is proposed and developed for condition assessment of bridges by classifying bridge elements into Primary, Secondary, Tertiary and Life Safety-Critical elements. Respective indices are calculated in addition to an overall bridge condition index. The HER framework enables combining different distress indicators and propagating both aleatory/epistemic uncertainties using either Dempster-Shafer or Yager's rule. Importance and reliability factors (collectively termed "credibility factor") are introduced based on bridge element importance and reliability of collected data. Second, QFD implementation has been demonstrated with the following applications: (i) Inspection Prioritization (ii) Decision-Making between Replacement and/ or Rehabilitation scenarios. For inspection prioritization, an Inspection House of Quality is prepared for translating consumer demands (WHATs) into inspection requirements (HOWs) and demonstrated using data developed from Colorado Department of Transportation (CDOT) inspection manual. For the decision-making scenarios, a case study is furnished for a bridge located in Victoria, BC. Finally, the infrastructure-user's expectations are dynamic given the changing economic conditions, technologies, environmental regulations, etc. A hidden Markov model (HMM) is utilized for predicting such dynamic customer response by using probabilities of focus areas that are of interest to the infrastructure-user as hidden parameters. Using the 2005 California Transportation's customer survey, a case study is presented for demonstrating the application. This new expert-based framework has an ability to enhance decision-making by addressing uncertainty in collected inspection data, facilitating customer input into MRR procedures and by predicting customer expectations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".