Advances in Studies in Artificial Intelligence for 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: 30 April 2027 | Viewed by 4185

Editors


E-Mail Website
Guest Editor
Department of Architectural Engineering, Ajou University, Suwon 16499, Republic of Korea
Interests: construction engineering and management; construction automation; smart construction
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Architectural Engineering, Dankook University, Yongin-si 16890, Gyeonggi-do, Republic of Korea
Interests: construction safety; smart construction; construction automation

Special Issue Information

Dear Colleagues,

The growing complexity and scale of construction projects are driving the need for more intelligent, adaptive, and data-driven approaches to project management. In this context, artificial intelligence (AI) has emerged as a powerful tool for innovation across the entire lifecycle of construction, from early-stage planning and design to on-site execution and post-construction operations and maintenance. AI technologies are being actively applied to enhance safety, productivity, quality control, and risk management, offering new opportunities to improve efficiency and resilience across diverse project environments. As these technologies begin to influence real-world construction practices, it becomes increasingly important to examine not only their technical capabilities, but also their practical applications, limitations, and implications for the future of construction management. This Special Issue seeks to bring together cutting-edge research and applied studies that demonstrate how AI can be meaningfully integrated into construction workflows to address industry challenges and improve project outcomes. We invite contributions that critically explore the transformative potential of AI in construction management; these submissions will contribute to shaping the next generation of intelligent and adaptive construction practices.

Dr. Byungjoo Choi
Dr. Hyunsoo Kim
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-anonymized 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
  • construction management
  • construction engineering
  • machine learning applications
  • computer vision
  • construction automation
  • large language models
  • generative AI

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

26 pages, 3171 KB  
Article
A Construction-Specific Framework Addressing Explainability, Liability, and Ethical Boundaries of AI Use in Civil Engineering
by Sharmin Jahan Badhan and Reihaneh Samsami
Buildings 2026, 16(14), 2759; https://doi.org/10.3390/buildings16142759 - 11 Jul 2026
Viewed by 516
Abstract
The Architecture, Engineering, and Construction (AEC) sector is increasingly adopting Artificial Intelligence (AI) technologies to enhance efficiency, decision-making, and operational performance across infrastructure and construction workflows. The objective of this study is to examine the challenges of implementing AI in civil and construction [...] Read more.
The Architecture, Engineering, and Construction (AEC) sector is increasingly adopting Artificial Intelligence (AI) technologies to enhance efficiency, decision-making, and operational performance across infrastructure and construction workflows. The objective of this study is to examine the challenges of implementing AI in civil and construction engineering, as associated with governance, ethics, and human interaction. The methodology integrates a structured conceptual framework developed through literature synthesis and exploratory survey analysis to assess the technical, governance, ethical, and sociotechnical implications of AI deployment in civil and construction engineering. The analysis focuses on key AI applications, including infrastructure inspection, bridge crack detection, construction safety monitoring, and predictive planning for maintenance and scheduling, while addressing critical dimensions such as explainability, liability, bias, ethical governance, and human factors. The results indicate that although AI-driven models can improve productivity and decision support, challenges related to transparency, accountability, data privacy, and overreliance on automated systems remain prevalent. The study concludes that the successful deployment of AI in civil and construction engineering requires rigorous validation practices, explainable and trustworthy system design, and sustained human-in-the-loop oversight to ensure responsible, reliable, and scalable integration into real-world engineering applications. Full article
Show Figures

Figure 1

Review

Jump to: Research, Other

26 pages, 3086 KB  
Review
How Finishing Materials Affect the Performance of Autonomous Mobile Robots?: An Exploratory Mixed-Method Review
by Jongwoo Cho, Byeongjun Lim, Minjae Kim and Tae Wan Kim
Buildings 2026, 16(12), 2438; https://doi.org/10.3390/buildings16122438 - 18 Jun 2026
Viewed by 303
Abstract
Although it is generally accepted that material characteristics influence the sensing and locomotion of autonomous mobile robots (AMRs), this knowledge is mostly anecdotal and remains fragmented. This study aims to shed light on the relationship between building finishing materials and AMR performance. To [...] Read more.
Although it is generally accepted that material characteristics influence the sensing and locomotion of autonomous mobile robots (AMRs), this knowledge is mostly anecdotal and remains fragmented. This study aims to shed light on the relationship between building finishing materials and AMR performance. To address the lack of literature on the subject, this exploratory mixed-methods review combines an AMR market survey, collection of failure cases, and review of robot navigation mechanisms. As a result, with additional expert assessment, this study derived a relational diagram containing five primary relationships for sensing (i.e., color on obstacle detection, texture and transparency on obstacle detection and mapping accuracy) and five for locomotion (i.e., slipperiness and unevenness on speed and path consistency, wheel-mark resistance on surface preservation). Potential research themes (e.g., sensitivity by robot specifications, BIM-based information utilization, and robot-specific signage systems) were also derived by thematic analysis. By establishing a foundational research framework that clarifies how architectural material choices dictate robotic reliability, this review contributes to designing experimental scenarios for future empirical validations in robot-inclusive spaces. Full article
Show Figures

Figure 1

Other

Jump to: Research, Review

34 pages, 1896 KB  
Systematic Review
Artificial Intelligence (AI) in Construction Management (CM): A Systematic Review of Models and Methods
by Niloofar Razi, Sharmin Jahan Badhan and Reihaneh Samsami
Buildings 2026, 16(11), 2225; https://doi.org/10.3390/buildings16112225 - 1 Jun 2026
Cited by 2 | Viewed by 2385
Abstract
Artificial Intelligence (AI) is revolutionizing Construction Management (CM) through automation, predictive analytics, and real-time decision-making throughout the project lifecycle.This study aims to provide a comprehensive and structured synthesis of AI models and their applications in CM. This paper presents a systematic review of [...] Read more.
Artificial Intelligence (AI) is revolutionizing Construction Management (CM) through automation, predictive analytics, and real-time decision-making throughout the project lifecycle.This study aims to provide a comprehensive and structured synthesis of AI models and their applications in CM. This paper presents a systematic review of 191 peer-reviewed articles published between 2020 and 2025, aiming to integrate the current state of AI implementation in CM, focusing on AI methods and models and their applications in CM. Compared to previous reviews that take these factors individually or focus narrowly on specific techniques, this study offers a comprehensive taxonomy that systematically maps AI techniques against CM functions and integration platforms. The results reveal that AI applications are primarily concentrated in risk and safety management, decision support, and monitoring and control, while domains such as legal analytics, robotics, and cybersecurity remain underexplored. Furthermore, Computer Vision (CV) and Deep Learning (DL) dominate tasks such as safety monitoring and defect detection, whereas Machine Learning (ML) and optimization algorithms are widely applied in cost estimation and scheduling. It also addresses developments rarely covered in construction research, including Generative AI (Gen-AI), Explainable AI (XAI), and transformer models, presenting a strategic framework for the widespread adoption of AI in the construction environment. This study contributes a structured taxonomy that systematically links AI models with CM functions and enabling technologies, providing a comprehensive synthesis of emerging trends and research gaps. Full article
Show Figures

Figure 1

Back to TopTop