Design, Fabrication and Construction in the Post-heuristic Era

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 10 May 2024 | Viewed by 1884

Special Issue Editors

Faculty of Architecture and Town Planning, Technion, Haifa 3200003, Israel
Interests: architecture; machine learning; computational design; digital fabrication; urban planning; sustainable architecture; BIM; graph neural networks
Faculty of Architecture and Town Planning, Technion—Israel Institute of Technology, Haifa 3200003, Israel
Interests: building; green building; architecture; building technology; sustainable construction; construction technology; sustainable architecture; green architecture; sustainability; theory of architecture
Special Issues, Collections and Topics in MDPI journals
Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 3200003, Israel
Interests: building information modelling (BIM); automated code checking (AC); building permitting; data-driven design and construction; construction management; digitalization in construction

Special Issue Information

Dear Colleagues,

Recent advances in machine learning herald the end of the age of the traditional algorithm. Heuristic programming, where computers were instructed using a fixed set of coded commands, is slowly giving way to computational models that learn how to solve problems on their own. Applied data science has reached the building industry, where it can participate in the design and production of the built environment.

This Special Issue will focus on machine learning and data-driven applications which are set to revolutionize the building industry. We are looking for new design and evaluation methods based on these technologies. We are interested in how these new methods fit into the building information modelling eco-system. We want to explore how learning can be used for the more physical aspects of fabrication, manufacturing, and construction. We are curious about the barriers which prevent the widespread adoption of this technology.

We invite you to contribute original papers describing post-heuristic methods and how they will change the way buildings are designed and built.

Dr. Guy Austern
Dr. Yasha Jacob Grobman
Dr. Tanya Bloch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • post-heuristic
  • computational design
  • machine learning
  • computer-aided manufacturing
  • artificial intelligence
  • architectural design
  • digital fabrication
  • building information modelling
  • construction management
  • digital fabrication

Published Papers (1 paper)

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Research

22 pages, 4629 KiB  
Article
Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification
by Guy Austern, Tanya Bloch and Yael Abulafia
Buildings 2024, 14(2), 527; https://doi.org/10.3390/buildings14020527 - 16 Feb 2024
Viewed by 1562
Abstract
The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This [...] Read more.
The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information into the ML models can improve classification accuracy. To test this, we created a graph data structure where each building element is represented as a node assigned with basic geometric information. The connections between the graph nodes (edges) represent the immediate neighbors of that node, capturing the contextual information expressed in the BIM model. We devised a process for extracting graphs from BIM files and used it to construct a graph dataset of over 42,000 building elements and used the data to train several types of ML models. We compared the classification results of models that rely only on geometry, to graph neural networks (GNNs) that leverage contextual information. This work demonstrates that graph-based models for building element classification generally outperform classic ML models. Furthermore, dividing the graphs that represent complete buildings into smaller subgraphs further improves classification accuracy. These results underscore the potential of leveraging contextual information via graphs for advancing ML capabilities in the BIM environment. Full article
(This article belongs to the Special Issue Design, Fabrication and Construction in the Post-heuristic Era)
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