Optimized Planning and Scheduling for Modular and Offsite \nConstruction
Notice bibliographique
Résumé
Offsite construction has gained momentum in recent years due to its improved performance in projects‘ schedule, quality, safety, and environmental impact without increasing cost. Several research studies have introduced planning and scheduling techniques for modular and offsite construction using Building Information Modeling (BIM) and simulation tools. In this research, a questionnaire survey was carried out in collaboration with the modular building institute (MBI), Niagara Relocatable Buildings Inc. (NRB Inc.), and the School of Building Science and Engineering at the University of Alberta. The questionnaire focused on two issues: (1) modular and offsite construction industry characteristics, and (2) barriers to increased market share in this industry. For the latter issue, effort was made to address five factors that emanated from the workshop on ―challenges and opportunities for modular construction in Canada,‖ held in October 2015, Montreal to analyze barriers to modular construction growth in Canada. Key findings of this questionnaire include requests for the use of a separate modular construction design code, innovative financing and insurance solutions, standards that consider procurement regulations, and lending institutions that partner with financial houses to create special lending programs for modular construction. Findings of this questionnaire were published on the official MBI website. \nThis research presents an alternative BIM-based integrated framework for modeling, planning, and scheduling of modular and offsite construction projects. BIM Vertex BD software was used in the proposed framework for automating data exchange between projects‘ BIM model and the proposed scheduling method. The proposed method integrated linear scheduling method (LSM), critical chain project management (CCPM), and the last planner system (LPS) into a comprehensive BIM-based framework for scheduling, monitoring, tracking, and controlling of projects while considering uncertainty associated with activity durations. A procedure for integrating offsite and onsite construction was introduced based on the proposed scheduling methodology. Then, a new multi-objective optimization model was developed using genetic algorithm (GA) to optimize the integration between the LSM and CCPM. This optimization model minimizes time, cost, and work interruptions simultaneously while considering uncertainty in productivity rates, quantities, and availability of resources. The developed model was based on the integration of six modules: 1) uncertainty and defuzzification module, 2) schedule calculations module, 3) cost calculations module, 4) optimization module, 5) module for identifying multiple critical sequences and schedule buffers, and 6) reporting module. Schedule buffers were assigned whether or not the optimized schedule allows for interruptions. This method considers delay and work interruption penalties and bonus payments. The developed integrated scheduling model for offsite and onsite construction was automated in newly developed software named ―Mod-Scheduler‖ using the ASP.NET system coded in C# programming language. A number of case studies were presented and analyzed to demonstrate the developed methodologies‘ features and capabilities. \nThis research also introduces a novel modular suitability index (MSI), which utilizes five indices; 1) connections index (CI), 2) transportation dimensions index (TDI), 3) transportation shipping distance index (TSDI), 4) crane cost penalty index (CCPI), and 5) concrete volume index (CVI). Calculating the MSI provided a unified indicator to assist in selecting near optimum module configurations for efficient planning of modular residential construction. \nThis research identifies the main factors affecting the configuration of modules in hybrid construction projects to introduce a new configuration model that is expected to assist hybrid construction stakeholders in identifying the most suitable configuration for each type of modules (i.e. panels) in their projects. A hybrid construction case study was selected to demonstrate the applicability of proposed model and to highlight its capabilities in selecting the most suitable configuration of panelized projects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».