Architectural Design Supported by Information Technology: 2nd Edition
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Architectural Design, Urban Science, and Real Estate".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 7194
Special Issue Editors
Interests: computational design; BIM; design decision support; AI; public participation
Special Issues, Collections and Topics in MDPI journals
Interests: robotic timber joinery; traditional timber framing; robotic fabrication; affordable housing design; constraint-based design
Special Issues, Collections and Topics in MDPI journals
Interests: mixed reality/AI/early design stages/design decision support/computational design
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As fundamental planning decisions are made during early stages in design processes, early stages of architectural design have a significant impact on the subsequent performance of cities, districts and buildings in these settlements.
Increasing digitization, the technological innovations that accompany digitization and resultant new methods have contributed to wide-ranging transformations in architectural design processes in recent decades.
Today, information technology offers a vast number of different design methods and tools, for example, simulations, artificial intelligence, additive manufacturing and robotic fabrication as well as BIM towards digital twins in built environments as a digital backbone.
In addition, climate change and its consequences have significantly changed the way we think about living together and how we deal with (spatial) resources.
The key to current research and research in the coming years will be to exploit the potential of currently available and forward-looking information technologies and their integration into design processes to offer an "expanded possibility space" and to support architects in decision-making processes.
The aim of this Special Issue is to allow scientists who are investigating digital methods to support decision-making processes in early stages of design processes to publish their works and to discuss potential application fields with a broad scientific community.
You can view the Original Special Issue here: https://www.mdpi.com/journal/buildings/special_issues/Architectural_Information.
Prof. Dr. Frank Petzold
Prof. Luis Felipe González Böhme
Dr. Gerhard Schubert
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
- early architectural design stages
- computational design
- design decision support
- simulation, prediction, and evaluation in design
- BIM
- performance-based spatial design
- machine learning
- Construction 4.0/robotic fabrication/human–robot collaboration
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Enabling cross-domain design decision support for additive construction
Authors: Chao Li; Frank Petzold
Affiliation: Chair of Architectural Informatics, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany.
Abstract: Advanced construction techniques, including additive manufacturing (AM) and modular construction, have drawn on experience in automation and digitalization from the manufacturing domain. Accordingly, existing ontological framework(s) from manufacturing should be studied and leveraged to enhance interoperability within the building industry, which is known to be fragmented with diverse areas of expertise, ranging from design, manufacturing to construction and quality control. A knowledge graph fusing such cross-domain expertise, once given, can be interacted using cutting-edge large language models for informative design decision support, improving design rationality regarding manufacturability, regulations, etc. However, it is rather labor-intensive to gather scattering knowledge pieces from experts, formalize dedicated domain ontologies for each, and then build up a coherent framework. To remedy the issue, this paper envisions a mixture-of-domain (MoD) approach by firstly postulating a well-founded ontology suit, then provide a prototype for documenting heterogeneous information entities from domain experts on BIM-based design. Eventually, a semi-formal knowledge graph compromising multitude of expertise will be generated. All in all, the proposed MoD approach will facilitate the interwinding knowledge acquisition processes and thus, bridge untapped area between formal and semi-formal knowledge realms, advancing future processes of ontology learning and retrieval augmented generation.