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Sustainability
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Engineering Safety Prevention and Sustainable Risk Management
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Engineering Safety Prevention and Sustainable Risk Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: 16 October 2025 | Viewed by 8163

Special Issue Editor

Prof. Dr. Murat Gunduz

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering, Qatar University, Qatar
Interests: safety management; risk management; construction project management; performance measurement

Special Issue Information

Dear Colleagues,

For many reasons, risk management and safety prevention are essential in engineering. Having poor safety and risk management systems can have negative effects on a company’s reputation, finances, and legal status. There may be large financial consequences, including cost overruns, project delays and quality issues. Fundamental to engineering practice, safety prevention and risk management guarantee not only life safety but also legal compliance, financial stability, a sustained reputation, higher productivity levels, project success, and environmental conservation.

Effective safety and risk management can also boost staff motivation and operational effectiveness. Industry professionals may optimize project workflows and reduce interruptions by detecting and addressing possible risks and hazards early on, which will eventually save time and money.  Furthermore, the effects of safety accidents can reach well beyond the immediate stakeholders in today’s linked world, where news spreads quickly through social media and internet platforms.

Furthermore, stakeholders anticipate that engineering firms will successfully manage risks in their operations given the growing emphasis on sustainability and corporate social responsibility. Through the incorporation of risk management into their operations, businesses can exhibit their dedication to sustainable development.

The aim of this Special Issue is to advance risk management and safety precautions for a sustainable environment. The foundations of engineering practice that support legal compliance, financial stability, reputation management, operational efficiency, worker well-being, environmental preservation, and social responsibility are safety prevention and risk management. Engineering companies may reduce risks, safeguard assets, and guarantee long-term success in a constantly changing global environment by giving priority to these factors.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • safety management;
  • risk management and decision making;
  • occupational health and safety;
  • sustainable risk management;
  • sustainable technologies for risk and safety management.

I look forward to receiving your contributions.

Prof. Dr. Murat Gunduz
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sustainability 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 2400 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

  • safety management
  • risk management
  • occupational health and safety
  • sustainability
  • hazards
  • risks

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Published Papers (5 papers)

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Research

19 pages, 1228 KiB  
Article
A Bayesian Belief Network Model for Assessing Financial Risk in PPP Healthcare Projects
by Alper Aslantas, Irem Dikmen and Mustafa Talat Birgonul
Sustainability 2025, 17(10), 4635; https://doi.org/10.3390/su17104635 - 19 May 2025
Viewed by 591
Abstract
Public-Private Partnerships (PPPs) are essential for accelerating sustainable development as they combine public goals with private sector efficiency, leading to improved service delivery and less financial burden on governments. It is a project delivery model based on long-term contractual arrangements, where the private [...] Read more.
Public-Private Partnerships (PPPs) are essential for accelerating sustainable development as they combine public goals with private sector efficiency, leading to improved service delivery and less financial burden on governments. It is a project delivery model based on long-term contractual arrangements, where the private sector provides services, including engineering, construction, and operation of public infrastructure, taking financial risks. At the project development stage, the private sector carries out a financial risk assessment to ensure economic returns from a PPP investment and secure funding for the project. In this paper, we present a Bayesian Belief Network (BBN)-based model that can be used to assess financial risks, particularly the level of profitability in PPP projects. The proposed model was developed considering PPP projects in the healthcare sector and validated using data on PPP hospital projects in Turkiye. The findings demonstrate that the BBN model is useful for capturing the interdependencies between risks, resulting in different scenarios, and provides effective decision support for investors in PPP projects. This study contributes to the literature by offering a novel application of probabilistic risk assessment to provide a better understanding of interrelated risk factors that may result in different financial scenarios. The model can be used by the private sector to assess risk, estimate profitability, and develop risk mitigation strategies in PPP healthcare projects, which may increase project success, contributing to social, environmental, and economic sustainability. Full article
(This article belongs to the Special Issue Engineering Safety Prevention and Sustainable Risk Management)
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30 pages, 1417 KiB  
Article
A Hybrid Model for Enhancing Risk Management and Operational Performance of AEC (Architectural, Engineering, and Construction) Consultants: An Integrated Partial Least Squares–Artificial Neural Network (PLS–ANN) Approach
by Hesham Ahmed Elsherbeny, Murat Gunduz and Latif Onur Ugur
Sustainability 2025, 17(4), 1467; https://doi.org/10.3390/su17041467 - 11 Feb 2025
Cited by 1 | Viewed by 1424
Abstract
The operational effectiveness of Architectural, Engineering, and Construction (AEC) consultants, whose services have a substantial impact on project execution and results, depends on effective risk management. Using 336 survey responses from professionals in the construction industry, such as consultants, contractors, and employers working [...] Read more.
The operational effectiveness of Architectural, Engineering, and Construction (AEC) consultants, whose services have a substantial impact on project execution and results, depends on effective risk management. Using 336 survey responses from professionals in the construction industry, such as consultants, contractors, and employers working on a range of infrastructure and building projects, this study validates a hybrid Partial Least Squares Structural Equation Modeling–Artificial Neural Network (–ANN) approach. In order to ensure both causal analysis and predictive insights for AEC consultant performance assessment, this study combines PLS–SEM and ANN to develop an integrated performance evaluation framework. While ANN ordered their relative relevance in a non-linear predictive model, the PLS–SEM analysis found that the two most important predictors of consultant performance were communication and relationship management (G03) and document and record management (G06). The hybrid approach is a more efficient and data-driven tool for evaluating AEC consultants than traditional regression models since it accurately captures both causal links and predictive performance. These results contribute to a robust and sustainable framework for performance evaluation in the AEC sector by offering practical insights into risk reduction and operational improvement. Full article
(This article belongs to the Special Issue Engineering Safety Prevention and Sustainable Risk Management)
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32 pages, 1452 KiB  
Article
Cultural Factors Impacting Health and Safety (H&S) Practices in a Developing Construction Economy
by Kashan Fayyaz, Muhammad Shahzaib, Arslan Aziz, Muhammad Irfan, Wesam Salah Alaloul and Muhammad Ali Musarat
Sustainability 2025, 17(3), 911; https://doi.org/10.3390/su17030911 - 23 Jan 2025
Cited by 1 | Viewed by 2105
Abstract
The study investigated the influence of cultural factors on Health and Safety (H&S) practices in the construction industry of a developing economy using a quantitative approach. Data were collected through a questionnaire survey from industry professionals. The findings reveal varying perceptions of safety [...] Read more.
The study investigated the influence of cultural factors on Health and Safety (H&S) practices in the construction industry of a developing economy using a quantitative approach. Data were collected through a questionnaire survey from industry professionals. The findings reveal varying perceptions of safety culture, communication, and practices, with mean scores ranging from 2.692 to 3.607. Safety training frequency showed high variability (mean = 2.692, CV = 43.13%, Skewness = 0.42, Z-score = −0.69, range = 1.531 to 3.853), while subcontractors’ safety compliance exhibited the least variability (mean = 3.589, CV = 26.50%, Skewness = −0.38, Z-score = 0.58, range = 2.638 to 4.540). Practices (mean = 3.327, CV = 25.69%, Skewness = −0.38), behaviors (mean = 3.234, CV = 27.40%, Skewness = −0.25), and norms (mean = 3.028, CV = 31.91%, Skewness = 0.10) also showed significant variations. Additionally, the key challenges with highest values include budget constraints (mean = 3.607, CV = 31.80%) and company rules (mean = 3.523, CV = 30.28%). Furthermore, Kruskal–Wallis’s test indicates statistically significant differences across variables, with medium to large effect sizes (η2). By addressing important cultural factors and challenges, the findings provide actionable insights to enhance worker safety, reduce accidents, and promote a safer working environment, thereby contributing to sustainable development and resilience in Pakistan’s construction sector. Full article
(This article belongs to the Special Issue Engineering Safety Prevention and Sustainable Risk Management)
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20 pages, 2100 KiB  
Article
Investigating the Interrelationships between Advanced Technologies and Safety Performance Factors: The Case of Higher Education Construction Projects
by Yasir Alhammadi, Mohammad S. Al-Mohammad and Rahimi A. Rahman
Sustainability 2024, 16(19), 8585; https://doi.org/10.3390/su16198585 - 3 Oct 2024
Viewed by 1635
Abstract
The architecture, engineering, and construction (AEC) industry faces ongoing challenges in enhancing safety performance. Despite the availability of advanced technologies for enhancing safety, there is limited understanding of the inter-relationships among safety factors and advanced technologies for enhancing safety performance. This study aims [...] Read more.
The architecture, engineering, and construction (AEC) industry faces ongoing challenges in enhancing safety performance. Despite the availability of advanced technologies for enhancing safety, there is limited understanding of the inter-relationships among safety factors and advanced technologies for enhancing safety performance. This study aims to investigate the inter-relationships among factors affecting safety performance and advanced technologies. A questionnaire survey was disseminated to construction professionals to assess the criticality of factors and strategies. The data were analyzed using descriptive statistics, correlation analysis, and exploratory factor analysis (EFA). The findings indicate that 16 factors and eight advanced technologies are critical for enhancing safety. The EFA grouped 11 critical factors into four underlying groupings: safety planning and hazard prevention, workplace environment and supervision, employee safety support, and medical readiness and site protection. Moreover, the EFA grouped the eight critical advanced technologies into two underlying groupings: advanced digital technologies and personal and site monitoring technologies. The correlation analysis demonstrates measurable but weak associations between the factors and advanced technologies, indicating the need for future research to explore additional variables that may impact these relationships. The findings help construction professionals prioritize resources to address the specific groupings of critical factors and advanced technologies. Full article
(This article belongs to the Special Issue Engineering Safety Prevention and Sustainable Risk Management)
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21 pages, 3523 KiB  
Article
Research on the Prediction of Sustainable Safety Production in Building Construction Based on Text Data
by Jifei Fan, Daopeng Wang, Ping Liu and Jiaming Xu
Sustainability 2024, 16(12), 5081; https://doi.org/10.3390/su16125081 - 14 Jun 2024
Cited by 4 | Viewed by 1569
Abstract
Given the complexity and variability of modern construction projects, safety risk management has become increasingly challenging, while traditional methods exhibit deficiencies in handling complex dynamic environments, particularly those involving unstructured text data. Consequently, this study proposes a text data-based risk prediction method for [...] Read more.
Given the complexity and variability of modern construction projects, safety risk management has become increasingly challenging, while traditional methods exhibit deficiencies in handling complex dynamic environments, particularly those involving unstructured text data. Consequently, this study proposes a text data-based risk prediction method for building construction safety. Initially, heuristic Chinese automatic word segmentation, which incorporates mutual information, information entropy statistics, and the TF-IDF algorithm, preprocesses text data to extract risk factor keywords and construct accident attribute variables. At the same time, the Spearman correlation coefficient is utilized to eliminate the multicollinearity between feature variables. Next, the XGBoost algorithm is employed to develop a model for predicting the risks associated with safe production. Its performance is optimized through three experimental scenarios. The results indicate that the model achieves satisfactory overall performance after hyperparameter tuning, with the prediction accuracy and F1 score reaching approximately 86%. Finally, the SHAP model interpretation technique identifies critical factors influencing the safety production risk in building construction, highlighting project managers’ attention to safety, government regulation, safety design, and emergency response as critical determinants of accident severity. The main objective of this study is to minimize human intervention in risk assessment and to construct a text data-based risk prediction model for building construction safety production using the rich empirical knowledge embedded in unstructured accident text, with the aim of reducing safety production accidents and promoting the sustainable development of construction safety in the industry. This model not only enables a paradigm shift toward intelligent risk control in safety production but also provides theoretical and practical insights into decision-making and technical support in safety production. Full article
(This article belongs to the Special Issue Engineering Safety Prevention and Sustainable Risk Management)
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