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Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects
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Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects

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

Deadline for manuscript submissions: 30 August 2026 | Viewed by 11891

Special Issue Editors

Dr. Syed Farhan Alam Zaidi

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Guest Editor
Construction Technology Innovation Laboratory, School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: construction safety; AI-driven monitoring; deep learning; natural language processing; computer vision; large language model
Dr. Akeem Pedro

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Guest Editor
Construction Technology Innovation Laboratory, School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: construction safety; safety education; construction informatics; virtual reality; augmented reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is transforming risk management in engineering and construction, offering predictive insights, automation, and real-time monitoring to enhance safety and efficiency. Traditional risk management relies on manual processes and historical data, often leading to inefficiencies and unforeseen hazards. AI-driven solutions, including machine learning, computer vision, and Internet of Things (IoT) integration, provide advanced capabilities for risk identification, assessment, and mitigation.

This Special Issue aims to explore the role of AI in modern risk management for engineering and construction projects. It will cover cutting-edge research and practical applications of AI technologies that enhance safety and reduce project uncertainties. We invite submissions that focus on AI-driven risk assessment models, digital twins for predictive risk analysis, autonomous monitoring systems, AI-powered safety compliance tools, and the integration of AI with Building Information Modeling (BIM), IoT, and drone-based surveillance.

Potential topics include, but are not limited to, the following:

  • AI-based predictive risk modeling;
  • Machine learning for hazard detection and accident prevention;
  • Integration of AI with BIM, IoT, and digital twins;
  • AI-driven safety compliance monitoring;
  • Computer vision for site safety analysis;
  • Autonomous systems for high-risk environments;
  • AI-powered decision support for risk-aware project management.

We welcome original research, case studies, and reviews from academia and industry to explore AI’s transformative impact on risk management. This issue aims to advance AI-driven solutions for safer, more resilient, and efficient engineering and construction practices.

Dr. Syed Farhan Alam Zaidi
Dr. Akeem Pedro
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

  • AI-driven risk management
  • computer vision for safety
  • autonomous monitoring systems
  • digital twins in engineering
  • safety compliance automation
  • risk assessment in construction
  • smart construction technologies
  • AI and building information modeling (BIM)
  • hazard detection and prevention
  • robotics in risk mitigation

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Published Papers (6 papers)

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Research

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24 pages, 1904 KB  
Article
AI-Driven Multi-Objective Optimization for Cost-Effective Design of Passive-Oriented Nearly Zero-Energy Building in Chengdu
by Chunjian Wang, Qidi Jiang, Jingshu Kong, Cheng Liu, Wenjun Hu and Jarek Kurnitski
Buildings 2026, 16(8), 1604; https://doi.org/10.3390/buildings16081604 - 18 Apr 2026
Viewed by 179
Abstract
The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction [...] Read more.
The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article
(This article belongs to the Special Issue Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects)
27 pages, 3478 KB  
Article
KLUE-BERT-Based Classification of Project Ownership in Korean Construction Accident Records for Comparative Safety Analysis of Public and Private Projects
by Hye Min Lee, Seung-Hyeon Shin, Jeong-Hun Won and Moon Gyu Kim
Buildings 2026, 16(7), 1393; https://doi.org/10.3390/buildings16071393 - 1 Apr 2026
Viewed by 277
Abstract
Project ownership is a critical factor that shapes safety management systems and accident patterns in construction. However, the Ministry of Employment and Labor (MOEL) industrial accident database, which is the largest construction accident database in Korea, does not include project ownership information. To [...] Read more.
Project ownership is a critical factor that shapes safety management systems and accident patterns in construction. However, the Ministry of Employment and Labor (MOEL) industrial accident database, which is the largest construction accident database in Korea, does not include project ownership information. To address this limitation, this study developed a fine-tuned KLUE-BERT framework that automatically classifies project ownership using unstructured text fields (site name, client name, and workplace name) in MOEL data. Training data were constructed through manual classification of the 2018–2023 approved statistics and data augmentation. The proposed model achieved high classification performance. Multilayered statistical analyses were conducted using the classified 2014–2023 construction accident data across six key accident variables: accident type, accident cause, construction scale, accident severity, occupation, and worker tenure. The results revealed statistically significant associations between project ownership and all six variables. Public projects exhibited relatively high proportions of accidents involving construction machinery and vehicles, whereas private projects exhibited higher proportions of fall- and scaffold-related accidents. This study presents a novel artificial intelligence-based framework that generates analytical variables absent from the original data and demonstrates its utility through large-scale statistical analysis. The findings provide empirical evidence to support the development of project ownership-specific construction safety policies. Limitations include potential data leakage from pre-split augmentation and generalizability limited to Korean construction data. Full article
(This article belongs to the Special Issue Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects)
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18 pages, 2229 KB  
Article
Large Language Models for Construction Risk Classification: A Comparative Study
by Abdolmajid Erfani and Hussein Khanjar
Buildings 2025, 15(18), 3379; https://doi.org/10.3390/buildings15183379 - 18 Sep 2025
Cited by 3 | Viewed by 4123
Abstract
Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The [...] Read more.
Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article
(This article belongs to the Special Issue Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects)
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28 pages, 4292 KB  
Article
Systematic Methodology for Estimating the Social Dimension of Construction Projects—Assessing Health and Safety Risks Based on Project Budget Analysis
by María D. Alba-Rodríguez, Valeriano Lucas-Ruiz and Madelyn Marrero
Buildings 2025, 15(13), 2313; https://doi.org/10.3390/buildings15132313 - 1 Jul 2025
Cited by 1 | Viewed by 1287
Abstract
One of the major challenges in the construction sector involves achieving sustainability in all three of its dimensions: economic, social, and environmental. Economic and environmental assessments have already been unified, but social indicators are still excluded. In this line, it is important for [...] Read more.
One of the major challenges in the construction sector involves achieving sustainability in all three of its dimensions: economic, social, and environmental. Economic and environmental assessments have already been unified, but social indicators are still excluded. In this line, it is important for a rapid introduction of sustainability indicators that the evaluations of its three dimensions are carried out simultaneously and without adding new training or a large workload to the project. In this work, it is proposed to use the definition of tasks in construction cost databases. These, due to their long tradition in the sector, have a clear definition of the contours of the problem and the inventory of resources. Therefore, based on this inventory that does not leave any unaccounted element, the evaluation of the social dimension is proposed through the use of the work units of the databases as an element of occupational risk assessment. The project cost and risk assessment are performed simultaneously in the construction of a social housing project in Andalusia, Spain. The costs of prevention measures represent 5% of the work units’ costs and reduce the risk indicator by 65%. Full article
(This article belongs to the Special Issue Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects)
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27 pages, 22501 KB  
Article
Computer Vision-Based Safety Monitoring of Mobile Scaffolding Integrating Depth Sensors
by Muhammad Sibtain Abbas, Rahat Hussain, Syed Farhan Alam Zaidi, Doyeop Lee and Chansik Park
Buildings 2025, 15(13), 2147; https://doi.org/10.3390/buildings15132147 - 20 Jun 2025
Cited by 8 | Viewed by 2700
Abstract
Mobile scaffolding is essential in construction but presents significant safety risks, particularly falls from height (FFH) due to improper use and insufficient monitoring. While prior research has identified hazards, it often lacks robust, actionable solutions, especially regarding the comprehensive analysis of worker behaviors [...] Read more.
Mobile scaffolding is essential in construction but presents significant safety risks, particularly falls from height (FFH) due to improper use and insufficient monitoring. While prior research has identified hazards, it often lacks robust, actionable solutions, especially regarding the comprehensive analysis of worker behaviors and the spatial context. This study proposed a computer vision-based safety monitoring system that leverages depth cameras for accurate spatial assessments and incorporates temporal conditions to reduce false alarms. The proposed system extends object detection algorithms with mathematical logic derived from safety rules to classify four key unsafe conditions related to safety helmet use, guardrail and outrigger presence, and worker overcrowding on mobile scaffolds. A diverse dataset from multiple sources enhances the model’s applicability to real-world scenarios, while a status trigger module verifies worker behavior over a 3 s window, minimizing detection errors. The experimental results demonstrate high precision (0.95), recall (0.97), F1-score (0.96), and accuracy (0.95) for safe behaviors, with similarly strong metrics for unsafe behaviors. The qualitative analysis further confirms substantial improvements in worker position detection and safety compliance using 3D data over 2D approaches. These findings highlight the effectiveness of the proposed system in improving mobile scaffolding safety, addressing critical research gaps, and advancing construction industry safety standards. Full article
(This article belongs to the Special Issue Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects)
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Review

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24 pages, 2123 KB  
Review
Artificial Intelligence for Predicting Insolvency in the Construction Industry—A Systematic Review and Empirical Feature Derivation
by Janappriya Jayawardana, Pabasara Wijeratne, Zora Vrcelj and Malindu Sandanayake
Buildings 2025, 15(17), 2988; https://doi.org/10.3390/buildings15172988 - 22 Aug 2025
Cited by 1 | Viewed by 2312
Abstract
The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined [...] Read more.
The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined a structured literature review with empirical analysis of construction sector-level insolvency data spanning the recent decade. A critical review of studies highlighted a clear shift from traditional statistical methods to AI/ML-driven approaches, with ensemble learning, neural networks, and hybrid learning models demonstrating superior predictive accuracy and robustness. While current predictive models mostly rely on financial ratio-based inputs, this research complements this foundation by introducing additional sector-specific variables. Empirical analysis reveals persistent patterns of distress, with micro- and small-sized construction businesses accounting for approximately 92% to 96% of insolvency cases each year in the Australian construction sector. Key risk signals such as firm size, cash flow risks, governance breaches and capital adequacy issues were translated into practical features that may enhance the predictive sensitivity of the existing models. The study also emphasises the need for digital self-assessment tools to support micro- and small-scale contractors in evaluating their financial health. By transforming predictive insights into accessible, real-time evaluations, such tools can facilitate early interventions and reduce the risk of insolvency among vulnerable construction firms. The current study combines insights from the review of AI/ML insolvency prediction models with sector-specific feature derivation, potentially providing a foundation for future research and practical adaptation in the construction context. Full article
(This article belongs to the Special Issue Next-Gen Risk Management: AI-Driven Solutions for Engineering and Construction Projects)
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