Generative AI in Architecture, Engineering and Construction: Innovations and Applications

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 952

Special Issue Editors


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Guest Editor
Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel
Interests: construction informatics; synergies between BIM and AI; robot-enabled digital twin systems; digitalization of building and infrastructure; sustainable building

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Guest Editor
Department of Diasater Mitigation for Structures, College of Civil Engneering, Tongji University, Shanghai 200092, China
Interests: AI in building energy management; smart building; sustainable building; building and renewable energy integration
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel
Interests: building information modeling (BIM); automated code compliance checking; semantic enrichment; digital building permitting; data-driven design; AI in AEC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Architectural, Engineering, and Construction (AEC) industry is undergoing a profound digital transformation that aims to improve the efficiency, effectiveness, and sustainability of building and infrastructure systems. This transformation relies on advanced automation technologies and tools for efficient data collection and processing, informed decision-making, and seamless collaboration among various stakeholders throughout the lifecycle of these systems.

In recent years, generative AI, particularly large language models, has demonstrated significant potential to address a wide range of challenges and unlock new applications across various industries, including AEC. Its capabilities, including human knowledge embedding, generic reasoning and problem-solving, multimodal data understanding and processing, and using and making tools, position it as a transformative technology for the AEC sector.

This Special Issue aims to collect papers focused on recent advancements in applying generative AI to accelerate the digital transformation of the AEC industry. We invite the submission of original research articles focused on theoretical and technological developments, real-world case studies, and critical reviews that explore the application of generative AI across all lifecycle stages of buildings and infrastructure, including design, construction, operation, and maintenance. We also welcome submissions that address ethical, regulatory, and governance aspects of generative AI in AEC, as well as those examining the impact, barriers, and risks associated with its adoption.

Dr. Huaquan Ying
Dr. Jianli Chen
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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • generative AI
  • large language models (LLMs)
  • architecture, engineering and construction (AEC)
  • multimodal data collection and processing
  • decision-making support
  • human–AI collaboration
  • ethical, regulatory, and governance aspects of generative AI

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Published Papers (1 paper)

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Research

29 pages, 1016 KB  
Article
Exploring the Relationship Between Generative AI and Employee Creativity in Construction Firms: A Hybrid PLS-SEM, IPMA, and fsQCA Approach
by Shiming Wang and Tailong Shi
Buildings 2026, 16(10), 1994; https://doi.org/10.3390/buildings16101994 - 18 May 2026
Viewed by 101
Abstract
Background: This study focuses on construction firms undergoing digital transformation, exploring the mechanisms through which Generative AI (GenAI) is associated with employee creativity in a context where knowledge is highly context-dependent, project teams are temporary, and unique safety and schedule pressures prevail. Methods: [...] Read more.
Background: This study focuses on construction firms undergoing digital transformation, exploring the mechanisms through which Generative AI (GenAI) is associated with employee creativity in a context where knowledge is highly context-dependent, project teams are temporary, and unique safety and schedule pressures prevail. Methods: A mixed-methods approach integrating Partial Least Squares Structural Equation Modeling (PLS-SEM), Importance-Performance Mapping Analysis (IPMA), and Fuzzy Set Qualitative Comparative Analysis (fsQCA) is employed. The proposed model is tested using primary survey data from 268 employees of Chinese construction firms. Results: Generative AI has no significant direct association with construction firm employee creativity (CFEC). Instead, it shows an indirect association through the full mediation of explicit knowledge sharing (EKS) and tacit knowledge sharing (TKS), with TKS having a stronger association with employee creativity. The relationship between GenAI and knowledge sharing is positively moderated by digital self-efficacy. The fsQCA identifies seven equifinal configurations leading to high employee creativity, with ‘explicit knowledge sharing and digital self-efficacy’ constituting the optimal configuration. Conclusions: Construction firms should actively promote knowledge sharing among their staff and provide regular training on GenAI tools, thereby fully harnessing employee creativity. Managerial Implication: Construction CEOs should prioritize building GenAI-supported knowledge sharing systems and improving employees’ digital self-efficacy, rather than expecting direct creativity improvement from GenAI deployment. Full article
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