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Applying Artificial Intelligence in Construction Management—2nd Edition
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Applying Artificial Intelligence in Construction Management—2nd Edition

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 589

Editors

Dr. Phuong Hoang Dat Nguyen

E-Mail Website
Guest Editor
Department of Construction and Concrete Industry Management, South Dakota State University, Brookings, SD 57007, USA
Interests: artificial intelligence; innovative project delivery and contracting methods; construction safety; automation in construction; construction productivity; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Minsoo Baek

E-Mail Website
Guest Editor
Department of Construction Management, Kennesaw State University, Marietta, GA 30060, USA
Interests: machine learning/artificial intelligence; construction analytics; risk management; innovative project delivery
Special Issues, Collections and Topics in MDPI journals
Dr. Md Nazmus Sakib

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is reshaping the construction industry through the integration of machine learning, computer vision, robotics, and large-scale data analytics. Construction management, which encompasses project planning, scheduling, cost estimation, resource allocation, risk assessment, safety, and quality control, stands to benefit substantially from these advances, given its reliance on coordinating complex and heterogeneous information under significant constraints.

Recent research has demonstrated the potential of AI to improve cost and schedule forecasting, automate progress and safety monitoring, support defect and hazard detection, and enhance the analysis of unstructured project documents through natural language processing and large language models. Coupled with digital twins, generative design, and robotics, these technologies offer opportunities to improve productivity, safety, and sustainability across the project lifecycle. Nevertheless, their adoption remains constrained by the limited availability of high-quality and interoperable datasets, workforce skill gaps, concerns regarding the transparency and reliability of AI-driven decisions, and emerging cybersecurity and data privacy risks.

This Special Issue (2nd Edition) invites original research articles, case studies, and critical reviews that advance both the methodological foundations and the practical implementation of AI in construction management. Topics of interest include, but are not limited to, the following:

  • AI, robotics, and automation in construction processes;
  • Machine learning, deep learning, and generative AI applications;
  • Computer vision for progress, safety, and quality monitoring;
  • Natural language processing and large language models for construction documentation;
  • Predictive analytics for cost, schedule, and risk management;
  • Intelligent decision support and optimization systems;
  • Sustainable and smart construction practices;
  • Digital twins, BIM integration, and AI-based simulation;
  • Explainable and trustworthy AI in construction;
  • Cyber–physical systems, cybersecurity, and data privacy.

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 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
  • 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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Related Special Issue

Published Papers (1 paper)

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Research

26 pages, 1721 KB  
Article
Interpretable Machine Learning for Classifying Expansion and Deceleration Regimes in the U.S. Housing Market Using Construction Cost and Supply Indicators
by Minsoo Baek and Jung-Hyun Lee
Buildings 2026, 16(15), 3000; https://doi.org/10.3390/buildings16153000 - 28 Jul 2026
Viewed by 348
Abstract
Housing market research has traditionally emphasized forecasting continuous price levels, often overlooking the discrete regime shifts that more directly capture cyclical risk and market turning points relevant to construction planning and investment decisions. This study develops an interpretable machine learning framework to forecast [...] Read more.
Housing market research has traditionally emphasized forecasting continuous price levels, often overlooking the discrete regime shifts that more directly capture cyclical risk and market turning points relevant to construction planning and investment decisions. This study develops an interpretable machine learning framework to forecast monthly U.S. housing market expansion and deceleration regimes one month ahead by integrating macroeconomic, financial, and construction-related indicators with national housing price data spanning January 1993 through March 2025. Feature selection and hyperparameter tuning are conducted entirely within the training sample using time-series cross-validation, ensuring that all reported performance metrics reflect genuine out-of-sample generalization. Recursive feature elimination combined with variance inflation factor screening yields a compact, seven-variable predictor set, with no macro-financial variable contributing an incremental discriminatory signal. Five classification models spanning linear and tree-based ensemble families are benchmarked under a strict temporal 80%:20% train–test split. Tree-based ensemble models consistently outperform the linear baseline, with Random Forest achieving the highest holdout AUC (0.932) and the most consistent deceleration detection across nested cross-validation folds. To ensure methodological transparency, explainable AI techniques, including SHapley Additive exPlanations, partial dependence, and individual conditional expectation analyses, are employed to interpret both global and local predictive associations underlying regime classification. Construction-related indicators, particularly Construction Put in Place and Building Permits at short lag horizons, emerge as the dominant supply-side predictive signals, outperforming macro-financial variables in regime discrimination by a margin of 0.184 in training CV AUC. By shifting the analytical focus from price forecasting to one-month-ahead regime prediction and integrating predictive accuracy with economic interpretability, this study provides a transparent and scalable framework for monitoring housing market cycles with direct applications to construction risk management, procurement timing, and project planning. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management—2nd Edition)
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