Advancing Construction Planning in Remote Regions: A Stochastic Time-Window Framework for Schedule and Cost Efficiency
Notice bibliographique
Résumé
Construction planning in remote regions is significantly challenged by stochastic time-window constraints induced by environmental and logistical factors. These constraints, such as limited access (e.g., no physical road), unpredictable weather (e.g., typhoons, blizzards, shifting freeze-thaw cycles, sand storms), and regulatory nature-based restrictions (e.g., season-based ecosystem protection regulations), introduce uncertainties that traditional construction planning methods like the Critical Path Method or Time-Cost Trade-off analysis are not designed to accommodate effectively. As a result, plans generated by such techniques are often flawed and infeasible, leading to persistent delays and cost overruns in projects undertaken in harsh, remote environments. Addressing these challenges is critical for infrastructure development, which has a socio-economic impact on remote communities. Furthermore, in the mining industry, often involving distant operations, the efficiency and feasibility of natural resource extraction can also be impacted by environmental and logistical constraints. Evidently, attempting the execution of construction projects in such settings almost certainly involves schedule delays and budget overruns. As traditional planning methods fall short, the demand for a robust strategy to tackle environmental uncertainties is especially evident in the context of climate change and unpredictable natural phenomena. A stochastic time-window framework has been developed to enhance construction planning and scheduling in remote settings. This framework integrates environment-induced constraints into the scheduling process, optimizes the selection of alternative execution methods for environment-sensitive activities, and quantifies the impact of stochastic time windows on project duration and total cost, thus addressing the shortcomings of conventional planning and cost optimization techniques. Initially, a Time-Window planning method was formulated to incorporate environmental constraints, validated via a case study of a river-crossing bridge in a remote northern region of Canada. Subsequently, a quantitative optimization framework was developed, employing enumerated simulation and an analytical reward function ranking alternative execution methods and crew configurations. Finally, a simulation-based approach utilizing the Monte Carlo method and historical meteorological and winter road access data was implemented to model the stochastic nature of environmental time windows for a project situated in northern Canada. The findings of this dissertation indicate that the proposed framework substantially improves schedule reliability over traditional methods in the settings of logistically complex and climate-sensitive regions. The Time-Window planning method ensures practical construction scheduling under environmental constraints, while the time-cost optimization framework identifies effective execution alternatives in parallel. Finally, the stochastic simulation of weather-related constraints reveals a likelihood of delays and budget overruns when compared to baseline estimates based on average records. These findings suggest that the stochastic time-window framework can be adopted as a valuable tool by construction planners in remote regions. By enabling more accurate scheduling, cost management, and risk budgeting, the framework enhances the feasibility of construction projects in geographically and environmentally challenging areas and advances the field of construction engineering and management.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».