Applying Artificial Intelligence in Construction Management
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".
Deadline for manuscript submissions: 1 December 2025 | Viewed by 60
Special Issue Editors
Interests: artificial intelligence; innovative project delivery and contracting methods; construction safety; automation in construction; construction productivity; data analytics
Interests: machine learning/artificial intelligence; construction analytics; risk management; innovative project delivery
Interests: artificial intelligence; data science; high-performance computing; signal processing; drone technology; virtual Reality training; augmented reality; mixed reality; building information modeling (BIM); digital twins
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) is revolutionizing the construction industry by integrating machine learning, robotics, and big data analytics to enhance efficiency, reduce costs, and improve safety. Construction management, a complex field involving project planning, resource allocation, risk management, and quality control, greatly benefits from AI-driven solutions. The key benefits of AI in construction management include enhanced accuracy in project scheduling and budgeting, increased worker safety through AI-driven monitoring systems, improved resource allocation and waste reduction, and faster construction timelines with AI-powered automation. However, the adoption of AI in construction management faces several challenges, including the limited availability of high-quality data, skill gaps, uncertainty in AI decision-making, and cybersecurity risks and data privacy concerns. Despite these challenges, the future of AI in construction management remains promising, with increasing advancements in AI-driven automation, robotics, and predictive analytics set to reshape the industry. In this regard, this Special Issue invites you to submit original research papers regarding “Applying Artificial Intelligence in Construction Management”. Topics may include, but are not limited to, the following:
- AI and automation in construction;
- Machine learning and deep learning;
- Computer vision and natural language processing;
- Predictive analytics;
- Decision support systems in construction;
- Sustainable and smart construction;
- Digital twins and AI simulation in construction;
- Cyber–physical systems and cybersecurity.
Dr. Phuong Hoang Dat Nguyen
Dr. Minsoo Baek
Dr. Md Nazmus Sakib
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 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
- artificial intelligence
- machine learning/deep learning
- computer vision
- natural language processing
- predictive analytics
- smart construction
- digital twins
- cyber–physical systems
- automation in construction
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