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Application of Digital Technology and AI in Construction Management
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Application of Digital Technology and AI 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: 15 July 2026 | Viewed by 1143

Special Issue Editors

Dr. Hongjiang Yao

E-Mail Website
Guest Editor
School of Management, Shandong University, Jinan 250100, China
Interests: construction project management; contractual governance; machine learning and large language models; project resilience; human–AI collaboration; supply chain management in construction projects
Dr. Junwei Zheng

E-Mail Website
Guest Editor
School of Public Policy and Administration, Nanchang University, Nanchang 330031, China
Interests: construction project management; engineering technology innovation; engineering project organizational behavior; occupational health and safety in construction industry; leadership
Dr. Yongshun Xu

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China
Interests: digital engineering management; emerging technologies and organizations (blockchain, smart contracts, artificial intelligence, etc.); project governance; smart construction and management; contract management
Dr. Haitao Wu

E-Mail Website
Guest Editor
School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
Interests: digital technology-enabled construction management; blockchain applications; human–AI collaboration; safety management; quality management

Special Issue Information

Dear Colleagues,

The global construction industry faces pressing demands for efficiency, sustainability, and safety, with traditional management practices struggling to keep pace. Digital technology and AI have emerged as transformative solutions to optimize project lifecycles. We invite original research and reviews for this Special Issue, “Application of Digital Technology and AI in Construction Management.”​

This Special Issue aims to provide a multidisciplinary platform to showcase innovative research, case studies, and methodological developments in the application of digital technology and AI. Its core objective is to advance our understanding of how emerging technologies—such as machine learning, big data analytics, digital twins, and blockchain—can be effectively integrated into construction management.​

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:​ AI-driven project management​; BIM for construction process optimization​; machine learning for site safety monitoring and quality control​; blockchain applications for supply chain transparency, contract management, and payment security​; sustainable construction management via AI; human–AI collaboration in construction project teams and change management; and innovation management​​.

We look forward to receiving your contributions.​

Dr. Hongjiang Yao
Dr. Junwei Zheng
Dr. Yongshun Xu
Dr. Haitao Wu
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

  • digital technology
  • artificial intelligence
  • ai-driven construction
  • construction management
  • building information modeling
  • project management
  • risk management
  • empirical studies
  • sustainable construction
  • case studies

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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

24 pages, 772 KB  
Article
Micro-Innovation in Construction Projects in the Digital Economy: The Role of Knowledge and Character Management
by Xiang Ao, Dengke Yu and Huan Xiao
Buildings 2026, 16(8), 1476; https://doi.org/10.3390/buildings16081476 - 9 Apr 2026
Viewed by 322
Abstract
In the context of the digital economy and quality engineering construction, micro-innovation has gained increasing attention in construction projects. This study incorporates knowledge and character management theory into micro-innovation research and explores how intellectual capital and team character shape micro-innovation in construction projects [...] Read more.
In the context of the digital economy and quality engineering construction, micro-innovation has gained increasing attention in construction projects. This study incorporates knowledge and character management theory into micro-innovation research and explores how intellectual capital and team character shape micro-innovation in construction projects within the digital economy. A questionnaire design was used to collect data from 315 projects, and the structural equation modeling was applied to test the conceptual model. The results reveal three main findings: (1) intellectual capital exerts a direct positive effect on micro-innovation in construction projects, whereas team character does not; (2) both intellectual capital and team character indirectly enhance micro-innovation through policy perception and market sensing; and (3) the mediating effect of market sensing is stronger in the relationship between intellectual capital and micro-innovation, while the mediating effect of policy perception is more prominent in the relationship between team character and micro-innovation. By theorizing the mechanisms of micro-innovation in construction projects, this study provides important implications for improving micro-innovation practices in the digital era. Full article
(This article belongs to the Special Issue Application of Digital Technology and AI in Construction Management)
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19 pages, 1123 KB  
Article
Comparative Evaluation of Voxel and Mesh Representations for Digital Defect Detection in Construction-Scale Additive Manufacturing
by Seyedali Mirmotalebi, Hyosoo Moon, Raymond C. Tesiero and Sadia Jahan Noor
Buildings 2026, 16(4), 805; https://doi.org/10.3390/buildings16040805 - 16 Feb 2026
Viewed by 504
Abstract
Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This [...] Read more.
Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article
(This article belongs to the Special Issue Application of Digital Technology and AI in Construction Management)
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