A Quality Function Deployment (QFD) Approach for Bridge Maintenance Management
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle