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Applying Artificial Intelligence in Construction Management
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Applying Artificial Intelligence 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: 31 March 2026 | Viewed by 5467

Special Issue 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
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
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 Issue Information

Dear Colleagues,

Artificial intelligence (AI) is revolutionizing the construction industry by integrating machine learning, robotics, and big data analytics to enhance efficiency, reduce costs, and improve safety. Construction management, a complex field involving project planning, resource allocation, risk management, and quality control, greatly benefits from AI-driven solutions. The key benefits of AI in construction management include enhanced accuracy in project scheduling and budgeting, increased worker safety through AI-driven monitoring systems, improved resource allocation and waste reduction, and faster construction timelines with AI-powered automation. However, the adoption of AI in construction management faces several challenges, including the limited availability of high-quality data, skill gaps, uncertainty in AI decision-making, and cybersecurity risks and data privacy concerns. Despite these challenges, the future of AI in construction management remains promising, with increasing advancements in AI-driven automation, robotics, and predictive analytics set to reshape the industry. In this regard, this Special Issue invites you to submit original research papers regarding “Applying Artificial Intelligence in Construction Management”. Topics may include, but are not limited to, the following:

  • AI and automation in construction;
  • Machine learning and deep learning;
  • Computer vision and natural language processing;
  • Predictive analytics;
  • Decision support systems in construction;
  • Sustainable and smart construction;
  • Digital twins and AI simulation in construction;
  • Cyber–physical systems and cybersecurity.

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-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

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

Published Papers (6 papers)

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Research

24 pages, 1628 KB  
Article
A Neuro-Symbolic Framework for Ensuring Deterministic Reliability in AI-Assisted Structural Engineering: The SYNAPSE Architecture
by Adriano Castagnone and Giuseppe Nitti
Buildings 2026, 16(3), 534; https://doi.org/10.3390/buildings16030534 - 28 Jan 2026
Viewed by 184
Abstract
This paper addresses the opportunities and risks of integrating Large Language Models (LLMs) into structural engineering. Exclusive reliance on LLMs is inadequate in this field, because their probabilistic nature can lead to hallucinations and inaccuracies that are unacceptable in safety-critical domains which require [...] Read more.
This paper addresses the opportunities and risks of integrating Large Language Models (LLMs) into structural engineering. Exclusive reliance on LLMs is inadequate in this field, because their probabilistic nature can lead to hallucinations and inaccuracies that are unacceptable in safety-critical domains which require rigorous calculations. To resolve this dilemma, we propose adopting Neuro-Symbolic Artificial Intelligence (NSAI), a hybrid approach that balances neural intuition with symbolic rigor. The NSAI architecture employs an intelligent query system to enrich user requests and delegate critical operations to deterministic external algorithms. This system is designed to enhance reliability and support regulatory compliance, as exemplified by the 3Muri chatbot case study, an NSAI (gemini-2.5-flash)-based intelligent assistant for structural analysis software. We developed 3Muri chatbot implementing AI processes. Our experimental results, based on over 200 questions submitted to the chatbot, show that this hybrid approach achieves 94% accuracy while keeping response times below 2 s. These results validate the feasibility of deploying AI systems in safety-critical engineering domains. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management)
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29 pages, 2446 KB  
Article
AI-Driven Automation of Construction Cost Estimation: Integrating BIM with Large Language Models
by Mohamed Abdelsalam, Amr Ashmawi and Phuong H. D. Nguyen
Buildings 2026, 16(3), 485; https://doi.org/10.3390/buildings16030485 - 24 Jan 2026
Viewed by 540
Abstract
The construction industry faces challenges in estimating costs because the processes are time-consuming and involve a high likelihood of making errors. For instance, quantity take-offs are often inaccurate, and there is not a simple way to integrate data from Building Information Modeling (BIM) [...] Read more.
The construction industry faces challenges in estimating costs because the processes are time-consuming and involve a high likelihood of making errors. For instance, quantity take-offs are often inaccurate, and there is not a simple way to integrate data from Building Information Modeling (BIM) platforms and cost databases. This study introduces a framework that utilizes the Model Context Protocol (MCP) to ensure seamless integration between large language models (LLMs) and BIM models through Autodesk Revit in order to enable fully automated cost estimation workflows. The developed system combines an AI-powered MCP server with cost databases that are standard in the industry, such as the 2025 Craftsman National Building Cost Manual and the ZIP code-based location modifiers. This system enables LLMs to automatically obtain quantities from BIM models, match components to cost items, make regional changes, and make professional cost estimates. A case study of estimating the cost of an electrical system shows that the framework can reduce estimation time from 2.5–3.5 h (manual baseline) to 42.3 ± 3.7 s (n = 5 runs, warm start), representing a 98.6% efficiency gain, while being more accurate with respect to industry standards. The system processed 187 BIM elements in three component groups (receptacles, conduits, and panels). It automatically matched them to the right cost database items, used location-specific modifiers for ZIP code 01003, and made a full cost estimate of USD 13,945.81 with detailed breakdowns and a percent difference of %5.1 of the manual estimation. This research enhances automation in construction by developing a methodology for AI-BIM integration using standardized protocols, shows the practical application of AI in construction workflows, and provides empirical evidence of the advantages of automation in cost estimation processes. The results indicate that MCP-based AI integration presents a novel approach for construction automation, delivering improvements while applying professional standards of accuracy and availability. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management)
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33 pages, 326 KB  
Article
Intelligent Risk Identification in Construction Projects: A Case Study of an AI-Based Framework
by Kristijan Vilibić, Zvonko Sigmund and Ivica Završki
Buildings 2026, 16(2), 409; https://doi.org/10.3390/buildings16020409 - 19 Jan 2026
Viewed by 290
Abstract
Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by minimizing disruptions, controlling costs, and enhancing decision-making efficiency. Early identification and mitigation of [...] Read more.
Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by minimizing disruptions, controlling costs, and enhancing decision-making efficiency. Early identification and mitigation of risks allow resources to be allocated where they have the greatest effect, thereby optimizing overall project outcomes. However, conventional methods such as expert judgment and probabilistic modeling often struggle to process extensive datasets and complex interdependencies among risk factors. This study explores the potential of an AI-based framework for risk identification, utilizing artificial intelligence to analyze project documentation and generate a preliminary set of identified risks. The proposed methodology is implemented on the ‘Trg pravde’ judicial infrastructure project in Zagreb, Croatia, applying AI models (GPT-5, Gemini 2.5, Sonnet 4.5) to identify phase-specific risks throughout the project lifecycle. The approach aims to improve the efficiency of risk identification, reduce human bias, and align with established project management methodologies such as PM2. Initial findings suggest that the use of AI may broaden the range of identified risks and support more structured risk analysis, indicating its potential value as a complementary tool in risk management processes. However, human expertise remains crucial for prioritization, contextual interpretation, and mitigation. The study demonstrates that AI augments, rather than replaces, traditional risk management practices, enabling more proactive and data-driven decision-making in construction projects. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management)
19 pages, 1467 KB  
Article
AI-Driven Process Mining for ESG Risk Assessment in Sustainable Management
by Riccardo Censi, Paola Campana, Francesco Bellini, Fulvio Schettino and Chiara De Pucchio
Buildings 2025, 15(23), 4260; https://doi.org/10.3390/buildings15234260 - 25 Nov 2025
Viewed by 913
Abstract
The construction sector faces growing challenges in integrating sustainability, risk management, and regulatory compliance, in line with initiatives such as the European Green Deal, the Corporate Sustainability Reporting Directive, and international building standards. However, the systematic adoption of ESG metrics in decision-making remains [...] Read more.
The construction sector faces growing challenges in integrating sustainability, risk management, and regulatory compliance, in line with initiatives such as the European Green Deal, the Corporate Sustainability Reporting Directive, and international building standards. However, the systematic adoption of ESG metrics in decision-making remains limited due to fragmented data, the lack of predictive tools, and reliance on static reporting. This study proposes and illustrates a digital framework, based on simulated data, that combines Artificial Intelligence, Process Mining, and Robotic Process Automation to enhance ESG risk assessment in sustainable construction management. The model, formalized through Business Process Model and Notation, integrates Machine Learning for risk weighting and classification, and leverages Web Scraping and Business Intelligence for dynamic data acquisition. A simulated case study involving 100 synthetic construction projects is used to demonstrate the internal logic and quantitative feasibility of the framework, showing how automated data integration and predictive modeling can improve the consistency of ESG risk identification and classification. While the results are illustrative rather than empirical, they confirm the analytical coherence and reproducibility of the proposed workflow. From a scientific perspective, it contributes an integrated methodology that bridges predictive analytics and process management for ESG evaluation. From a practical standpoint, it offers a structured and reproducible workflow to anticipate, classify, and mitigate ESG risks, supporting the construction sector’s transition toward data-driven and sustainability-first management practices. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management)
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31 pages, 948 KB  
Article
Investment Risk Analysis of Municipal Railway Construction Projects Based on Improved SNA Methodology
by Rupeng Ren, Guilongjie Hu, Jun Fang, Xiaoqing Tong and Chengrui Wang
Buildings 2025, 15(22), 4025; https://doi.org/10.3390/buildings15224025 - 7 Nov 2025
Cited by 2 | Viewed by 686
Abstract
By analyzing all types of risks in the investment process of a municipal railroad construction project, 16 investment risk factors are extracted, and a network of investment risk factors and a comprehensive impact matrix of the project are constructed by comprehensively applying social [...] Read more.
By analyzing all types of risks in the investment process of a municipal railroad construction project, 16 investment risk factors are extracted, and a network of investment risk factors and a comprehensive impact matrix of the project are constructed by comprehensively applying social network analysis (SNA) and the decision-making test and evaluation laboratory (DEMATEL) method. By analyzing the point centrality, proximity centrality and intermediate centrality of the SNA network, core risk factors such as insufficient operation and management level (degree centrality: 51.111) and cost overruns (in-closeness centrality: 93.75) are identified; through the correlation strength analysis of risk factors via the DEMATEL method, “policy–approval–schedule–cost” is clearly identified. Moreover, through the DEMATEL method, correlation intensity analysis between risk factors was clarified, and six key risk transmission paths were identified, such as “policy–approval–duration–cost”, “market–cost–operation”, etc., among which the cumulative impact coefficient of the “market–cost–operation” path reached 0.664. According to the results of the analysis of core risk factors and key risk transmission paths, targeted investment risk response proposals for municipal railroad construction projects are put forward with regard to four aspects: strengthening the control of core driving factors, curbing the deterioration of key results factors, blocking the risk of intermediate conduction factors, and resisting the impact of marginal risk factors. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management)
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22 pages, 7355 KB  
Article
Monitoring Progress and Standardization of Work Using Artificial Intelligence—Evolution of NORMENG Project
by Zvonko Sigmund, Kristijan Vilibić, Ivica Završki and Matej Mihić
Buildings 2025, 15(21), 3844; https://doi.org/10.3390/buildings15213844 - 24 Oct 2025
Viewed by 1954
Abstract
This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to [...] Read more.
This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to detect materials and estimate quantities, aiming to assess whether a broad, context-aware AI system can match the precision of specialized, domain-specific tools or even human work needed for productivity estimations. While the AI demonstrated potential for basic entity detection and preliminary quantity estimations, it showed significant limitations in delivering fine-grained, temporally accurate breakdowns without targeted adaptation. The findings underscore the need for domain-specific fine-tuning and human-in-the-loop validation to transform AI into a reliable tool for construction management. This initial contribution provides empirical insights and actionable recommendations for advancing automated progress monitoring in the construction sector. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence in Construction Management)
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