Identifying the Causes of Delay Using the Analytic Hierarchy Process (AHP) Method in Brazilian Public Road Infrastructure Projects
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Notice bibliographique
Résumé
The highway infrastructure system plays an important role in a country whose continental extensions require an adequate transportation system to connect people and places and boost the economy. Delayed delivery of these projects is one of the most significant problems in the road construction industry and poses challenges to project success in terms of time, cost, quality, and safety. Some studies have carried out literature reviews, applied questionnaires and performed expert interviews, or used analytical methods such as machine learning and AHP (Analytic Hieraquircal Process) to identify factors that cause delays. The objective of this study was to identify the main delay factors in road infrastructure projects using the AHP analytic hierarchy process approach in the context of public works management in Brazil. This study consisted of two stages, the first being a search in databases and search engines, using keywords that point to studies on delay factors in road construction projects. After this, the criteria and sub-criteria were examined and classified, and the factors and causes of delays were ranked based on discussions with an expert and the application of a questionnaire, according to the AHP methodology. The supporting software used was SuperDecisions. The main factors (criteria) and sub-factors (sub-criteria) that influence delay were categorized according to the literature. The main factors were compiled into five criteria called: principal contractor, designer, manager, material/manpower/equipment, and external factors. Within these groups, 24 sub-factors that most influenced delay were initially selected. But after the consistency tests were not acceptable, a new selection was made, which considered in the analysis the 15 most influential sub-factors. According to the experts, the order of importance was: Contractor>External Factors>Materials>Manpower and Equipment>Manager>Designer. The most important sub-criterion according to the specialists in causing delays in Brazilian road infrastructure projects was climate. The study pointed out which factors should have priority in decision-making to avoid delays in public, government-funded road transportation projects in Brazil. By applying it, one can arrive at the variables that can be used to develop a prediction model that helps mitigate the risks of delay in public road infrastructure projects.
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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 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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