PAAD: Panelization algorithm for architectural designs.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 28 03 2024
accepted: 29 04 2024
medline: 11 6 2024
pubmed: 11 6 2024
entrez: 11 6 2024
Statut: epublish

Résumé

Due to the competitive nature of the construction industry, the efficiency of requirement analysis is important in enhancing client satisfaction and a company's reputation. For example, determining the optimal configuration of panels (generally called panelization) that form the structure of a building is one aspect of cost estimation. However, existing methods typically rely on rule-based approaches that may lead to suboptimal material usage, particularly in complex designs featuring angled walls and openings. Such inefficiency can increase costs and environmental impact due to unnecessary material waste. To address these challenges, this research proposes a Panelization Algorithm for Architectural Designs, referred to as PAAD, which utilizes a genetic evolutionary strategy built on the 2D bin packing problem. This method is designed to balance between strict adherence to manufacturing constraints and the objective of optimizing material usage. PAAD starts with multiple potential solutions within the predefined problem space, facilitating dynamic exploration of panel configurations. It approaches structural rules as flexible constraints, making necessary corrections in post-processing, and through iterative developments, the algorithm refines panel sets to minimize material use. The methodology is validated through an analysis against an industry implementation and expert-derived solutions, highlighting PAAD's ability to surpass existing results and reduce the need for manual corrections. Additionally, to motivate future research, a synthetic data generator, the architectural drawing encodings used, and a preliminary interface are also introduced. This not only highlights the algorithm's practical applicability but also encourages its use in real-world scenarios.

Identifiants

pubmed: 38861492
doi: 10.1371/journal.pone.0303646
pii: PONE-D-24-12621
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0303646

Informations de copyright

Copyright: © 2024 Fisher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Andrew Fisher (A)

Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada.

Xing Tan (X)

Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada.

Muntasir Billah (M)

Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada.

Pawan Lingras (P)

Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada.

Jimmy Huang (J)

School of Information Technology, York University, Toronto, Ontario, Canada.

Vijay Mago (V)

School of Health Policy and Management, York University, Toronto, Ontario, Canada.

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