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Digital Technologies, AI and BIM in Construction
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Digital Technologies, AI and BIM in Construction

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 5743

Special Issue Editor

Dr. Saeed Reza Mohandes

E-Mail Website
Guest Editor
Department of Civil Engineering and Management, School of Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: AI; deep learning; machine learning; fuzzy logics; meta-heuristics algorithms; probabilistic techniques; construction engineering and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The construction industry is rapidly transforming due to the use of digital technologies, artificial intelligence (AI), and building information modelling (BIM). These advancements are revolutionizing traditional practices, enabling smarter decision-making, improving efficiency, and enhancing sustainability. From design and planning to construction and maintenance, these technologies address challenges like safety risks, cost overruns, and collaboration inefficiencies.

This Special Issue explores cutting-edge developments in the digital technologies, AI, and BIM used in construction. It invites researchers and practitioners to share innovative methodologies, case studies, and frameworks that demonstrate the transformative potential of these tools in shaping a smarter and more sustainable built environment.

Topics of interest include the following:

  • AI applications for predictive analytics, safety, and resource optimization.
  • Industry 4.0 technologies in construction (e.g., IoT, blockchain, UAV, robotics, AR/VR, and digital twins).
  • Digital tools for sustainability and circular economy practices.
  • Challenges in and enablers of the adoption of digital technology in construction.
  • Automation and human–robot collaboration in modular construction.
  • BIM-based frameworks for safety, quality, and lifecycle management.

This Special Issue welcomes original research, reviews, and case studies that bridge research and practice, offering insights and solutions that will advance digital innovation in the construction sector.

Dr. Saeed Reza Mohandes
Guest Editor

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

  • digital technologies in construction
  • artificial intelligence in construction
  • building information modelling (BIM)
  • Industry 4.0 and 5.0 in construction
  • construction automation
  • digital twins in construction
  • human–robot collaboration
  • sustainability in construction
  • smart construction practices

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

Published Papers (3 papers)

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Research

25 pages, 1392 KB  
Article
Barriers, Enablers, and Adoption Patterns of IoT and Wearable Devices in the Saudi Construction Industry: Survey Evidence
by Ibrahim Mosly
Buildings 2026, 16(2), 347; https://doi.org/10.3390/buildings16020347 - 14 Jan 2026
Viewed by 912
Abstract
The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful [...] Read more.
The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful implementation. A structured questionnaire was distributed to 567 construction professionals across different roles and projects. Frequency analysis was used to study adoption patterns, chi-square tests to study demographic factors, and principal component analysis for exploratory factor analysis to discover hidden adoption factors. The findings show that smart safety vests and helmets receive the highest level of recognition. On the other hand, advanced monitoring systems, including fatigue and environmental sensors, are not used enough. Group differences in device adoption were investigated in terms of years of experience, academic qualification, job role, and project budget. The findings from factor analysis show that three main factors determine adoption rates, which include (1) safety and operational effectiveness, (2) worker acceptance and support structures, and (3) technical and adoption barriers. A data-driven system is created to help policymakers and industry leaders accelerate construction safety digitalization efforts. Full article
(This article belongs to the Special Issue Digital Technologies, AI and BIM in Construction)
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28 pages, 4508 KB  
Article
Mixed Reality-Based Multi-Scenario Visualization and Control in Automated Terminals: A Middleware and Digital Twin Driven Approach
by Yubo Wang, Enyu Zhang, Ang Yang, Keshuang Du and Jing Gao
Buildings 2025, 15(21), 3879; https://doi.org/10.3390/buildings15213879 - 27 Oct 2025
Cited by 1 | Viewed by 1642
Abstract
This study presents a Digital Twin–Mixed Reality (DT–MR) framework for the immersive and interactive supervision of automated container terminals (ACTs), addressing the fragmented data and limited situational awareness of conventional 2D monitoring systems. The framework employs a middleware-centric architecture that integrates heterogeneous [...] Read more.
This study presents a Digital Twin–Mixed Reality (DT–MR) framework for the immersive and interactive supervision of automated container terminals (ACTs), addressing the fragmented data and limited situational awareness of conventional 2D monitoring systems. The framework employs a middleware-centric architecture that integrates heterogeneous subsystems—covering terminal operation, equipment control, and information management—through standardized industrial communication protocols. It ensures synchronized timestamps and delivers semantically aligned, low-latency data streams to a multi-scale Digital Twin developed in Unity. The twin applies level-of-detail modeling, spatial anchoring, and coordinate alignment (from Industry Foundation Classes (IFCs) to east–north–up (ENU) coordinates and Unity space) for accurate registration with physical assets, while a Microsoft HoloLens 2 device provides an intuitive Mixed Reality interface that combines gaze, gesture, and voice commands with built-in safety interlocks for secure human–machine interaction. Quantitative performance benchmarks—latency ≤100 ms, status refresh ≤1 s, and throughput ≥10,000 events/s—were met through targeted engineering and validated using representative scenarios of quay crane alignment and automated guided vehicle (AGV) rerouting, demonstrating improved anomaly detection, reduced decision latency, and enhanced operational resilience. The proposed DT–MR pipeline establishes a reproducible and extensible foundation for real-time, human-in-the-loop supervision across ports, airports, and other large-scale smart infrastructures. Full article
(This article belongs to the Special Issue Digital Technologies, AI and BIM in Construction)
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31 pages, 2262 KB  
Article
Risk Assessment and Mitigation Strategies in Green Building Construction Projects: A Global Empirical Study
by Saeed Reza Mohandes, Ridwan Taiwo, Abdul-Mugis Yussif, Tong Han, Faris Elghaish, Mehrdad Arashpour, Atul Kumar Singh and Mary Subaja Christo
Buildings 2025, 15(19), 3485; https://doi.org/10.3390/buildings15193485 - 26 Sep 2025
Cited by 1 | Viewed by 2297
Abstract
The construction industry significantly impacts environmental degradation, making sustainable practices like green building construction projects (GBCPs) essential. Although GBCPs offer substantial benefits, they also come with unique risks related to their sustainable nature and common construction challenges. Research on GBCP risks is often [...] Read more.
The construction industry significantly impacts environmental degradation, making sustainable practices like green building construction projects (GBCPs) essential. Although GBCPs offer substantial benefits, they also come with unique risks related to their sustainable nature and common construction challenges. Research on GBCP risks is often fragmented, lacks proper classification, and misses a global perspective, with insufficient focus on empirical assessment and risk mitigation strategies. This study addresses these gaps by systematically identifying risks associated with GBCPs, empirically assessing them using data from global experts, and proposing mitigation strategies. Utilising reliability tests, descriptive statistics, one-sample t-tests, hypothesis testing, and correlation analysis, 42 risk factors were determined and assigned to nine groups: legal and regulatory, technical, financial, material-related, design, schedule and planning, communication and awareness, performance and operational, and environmental. Green product certification and re-evaluation charges, client finance difficulties, the high cost of green materials and equipment, the absence of qualified project teams, and additional expenditures for green building design and construction are the top five concerns. The study also identifies 45 mitigation strategies, enhancing understanding of GBCP risks and guiding stakeholders in effective risk management and sustainable construction practices. Full article
(This article belongs to the Special Issue Digital Technologies, AI and BIM in Construction)
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