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Digital Transformation in Construction Management
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Digital Transformation in Construction Management

A Special Issue of Buildings (ISSN 2075-5309) belonging to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1509

Editors

Dr. Mohamed Yamany

E-Mail Website
Guest Editor
Department of Engineering and Technology, East Texas A&M University, Commerce, TX 75429, USA
Interests: construction project management; infrastructure asset management; artificial intelligence; machine learning; deep learning; virtual reality; BIM technologies
Dr. Mohamed T. Elnabwy

E-Mail Website
Guest Editor
School of Architecture and Built Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Interests: construction management; artificial intelligence in construction and infrastructure; digital transformation and construction informatics; construction waste management and circular economy; smart infrastructure and structural health monitoring
Dr. Ahmed Abdelaty

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering and Construction Management, Univeristy of Wyoming, Laramie, WY 82071, USA
Interests: digital project delivery; data analytics in construction management; preconstruction services
Dr. Hosam Hegazy

E-Mail Website
Guest Editor
Department of Engineering and Technology Management, University of Louisiana at Lafayette, Lafayette, LA 70503, USA
Interests: digital transformation in construction management; building information modeling (BIM); digital twins; artificial intelligence and machine learning in construction; infrastructure and energy project delivery; data-driven decision support systems; sustainability and smart construction technologies

Special Issue Information

Dear Colleagues,

The construction industry is entering a new era of digitally enabled construction management. From BIM and digital twins to AI/ML, IoT sensing, cloud and mobile platforms, AR/VR, robotics and automation, blockchain, and advanced analytics, emerging technologies are reshaping how projects are planned, delivered, monitored, and operated, thus unlocking gains in productivity, cost and schedule performance, safety, quality, risk control, and sustainability across the project and asset lifecycle.

But digital transformation is not simply “adopting tools.” Real impact comes from connecting data, people, and workflows—rethinking processes, interoperability, governance, standards, and organizational capabilities to enable integrated, end-to-end decision-making. Despite rapid progress, research and practice often remain siloed, focusing on single technologies or isolated functions, with limited guidance on how to embed digital strategies systematically into everyday construction management.

This Special Issue, “Digital Transformation in Construction Management,” welcomes high-impact, solution-oriented contributions that advance both theory and practice. We invite original research, implementation frameworks, methods, and real-world case studies demonstrating how digital technologies—individually or in combination—transform construction management functions, including: planning and scheduling, cost and productivity control, field operations, safety and quality management, supply chain and procurement, contracts/claims, project controls, workforce and equipment management, sustainability and resilience, and lifecycle asset/facilities management.

We particularly encourage papers with validated results and practical lessons that show not only what works, but how to implement digital transformation effectively.

Dr. Mohamed Yamany
Dr. Mohamed T. Elnabwy
Dr. Ahmed Abdelaty
Dr. Hosam Hegazy
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

  • digital transformation in construction management
  • building information modeling (BIM)
  • digital twins
  • artificial intelligence (AI) and machine learning
  • IoT and smart construction (sensors/wearables)
  • AR/VR for construction applications
  • industry 4.0–enabled project delivery (integration/interoperability)

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

Published Papers (2 papers)

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Research

29 pages, 4219 KB  
Article
Level of Naming Compliance: Syntactic, Referential, and LLM-Supported Semantic Validation of BIM/CDE File Names
by Jędrzej Pasalski, Tomasz Owerko, Karolina Tomaszkiewicz and Mateusz Kasznia
Buildings 2026, 16(14), 2886; https://doi.org/10.3390/buildings16142886 - 20 Jul 2026
Viewed by 453
Abstract
Inconsistent and erroneous file naming is a recurring information management problem in BIM and Common Data Environment (CDE) workflows, affecting document identification, retrieval, traceability, and control. This problem was observed during work on a real-world modular railway station project, where received documentation included [...] Read more.
Inconsistent and erroneous file naming is a recurring information management problem in BIM and Common Data Environment (CDE) workflows, affecting document identification, retrieval, traceability, and control. This problem was observed during work on a real-world modular railway station project, where received documentation included files prepared under heterogeneous naming practices. This paper proposes the Level of Naming Compliance (LoNC), a multi-level framework for assessing file name compliance in BIM/CDE environments. The framework evaluates file names as structured information containers and combines three validation stages: syntactic validation using regular expressions, referential validation against project-specific lookup tables, and LLM-supported semantic validation comparing selected decoded file name fields with descriptive document titles. In the current implementation, semantic validation was limited to discipline and document type. The proposed approach was evaluated using a controlled synthetic validation dataset of 500 file-title pairs, developed from inconsistency types observed in the motivating project. The dataset contained predefined examples of syntactic errors, invalid reference values, semantic inconsistencies, and fully compliant cases. Within this controlled evaluation set, the validator showed full agreement with the predefined expected LoNC levels. Because the dataset contained an unequal distribution of compliance classes, the evaluation was supplemented with balanced accuracy and class-specific performance measures. The results indicate that the implemented workflow correctly operationalises the cumulative LoNC framework under the tested conditions. The findings demonstrate that LoNC provides a more diagnostic assessment than binary file name checking by identifying the stage and type of non-compliance. The approach can support CDE managers and document controllers in quality assurance processes, while future work should validate it on larger real-world datasets and extend semantic validation beyond the selected fields. Full article
(This article belongs to the Special Issue Digital Transformation in Construction Management)
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26 pages, 4800 KB  
Article
Comparative Evaluation of YOLO Models for Real-Time Detection of Multiple Construction Resources Using Single- and Multi-Source Data
by Mohamed S. Yamany, Mohamed F. Ghozi, Rana Khallaf and Hany Abd Elshakour Mohamed
Buildings 2026, 16(13), 2565; https://doi.org/10.3390/buildings16132565 - 27 Jun 2026
Viewed by 592
Abstract
The evolution of technology facilitates efficient construction-site monitoring, hence improving the assessment of project performance and resource utilization. You Only Look Once (YOLO) algorithms are employed for real-time object detection; however, their applicability for multiple construction resources is limited, their performance requires enhancement, [...] Read more.
The evolution of technology facilitates efficient construction-site monitoring, hence improving the assessment of project performance and resource utilization. You Only Look Once (YOLO) algorithms are employed for real-time object detection; however, their applicability for multiple construction resources is limited, their performance requires enhancement, and their tendency to overfit necessitates further investigation. This paper uses recent YOLO algorithms to develop real-time object detection models for recognizing three construction resource categories. This study evaluates newly established YOLO algorithms and conducts a cross-source generalization analysis utilizing single- and multi-source datasets to boost model adaptability across various construction environments. Three different datasets of construction resource images were collected, preprocessed, and compiled. Six YOLO models (YOLOv8–12 and YOLO26) were trained, validated, and tested for accuracy, overfitting, and generalizability. The developed models demonstrate exceptional performance in real-time detection of three construction resources, with machine detection being the most efficient. The leading models, YOLO26 and YOLOv9, have mean Average Precision (mAP) scores of 0.88 and 0.87, respectively. The preliminary findings indicate that multi-source YOLO models outperform single-source ones, exhibiting superior generalization with an approximate mAP improvement of 23%, particularly when tested on data from environments and distributions different from the training dataset. This research advances the application of innovative technologies for effective resource and construction-site management. Full article
(This article belongs to the Special Issue Digital Transformation in Construction Management)
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