Evaluating Safety Training's Role in Reducing Construction Accidents: Poisson and Weibull Model Analysis

Yıl 2025, Cilt: 9 Sayı: 3, 133 - 156, 31.12.2025

Aydın Oğuz , Osman Hansu

https://doi.org/10.33720/kisgd.1627168

Öz

This study examines how safety training affects both the frequency of workplace accidents and the duration of accident-free intervals in high-risk construction settings. Based on a 36-month dataset from six major firms, workers were divided into four groups according to training type: specialized, general, informal, or none. Using a combined methodological approach—Poisson regression, multivariate Poisson modeling, and Weibull survival analysis—the study quantifies the impact of training on both the likelihood and timing of accidents. Specialized training was associated with up to a 50% reduction in accident frequency and nearly doubled the length of safe intervals (k < 1), particularly in high-rise construction projects. General training showed moderate effects, while informal or absent training had minimal impact, with hazard rates remaining stable or rising. Drawing on Heinrich’s Safety Pyramid and Reason’s Swiss Cheese Model, and validated through AIC, BIC, and likelihood ratio tests, the findings suggest that structured safety programs lead to meaningful, lasting behavioral change. The study advocates for task-specific, technology-enhanced training—such as virtual reality and wearables—as essential components of modern safety strategies in complex construction environments.

Anahtar Kelimeler

Construction Safety , Survival Analysis , Safety Training , Accident Reduction , Statistical Modeling

İnşaat Kazalarının Azaltılmasında Güvenlik Eğitiminin Rolünün Değerlendirilmesi: Poisson ve Weibull Model Analizi

Öz

Bu çalışma, güvenlik eğitiminin yüksek riskli inşaat ortamlarında iş kazalarının sıklığı ve kazasız geçen süreler üzerindeki etkisini incelemektedir. Altı büyük inşaat firmasından 36 aylık bir veri setine dayanarak, işçiler aldıkları eğitim türüne göre dört gruba ayrılmıştır: uzmanlaşmış, genel, gayri resmi ve hiç eğitim almayanlar. Poisson regresyonu, çok değişkenli Poisson modellemesi ve Weibull sağkalım analizi gibi birleşik bir metodolojik yaklaşım kullanılarak, eğitimin kazaların olasılığı ve zamanlaması üzerindeki etkisi nicel olarak değerlendirilmiştir. Uzmanlaşmış eğitim, özellikle yüksek katlı inşaat projelerinde olmak üzere, kaza sıklığında %50’ye varan bir azalma ve kazasız geçen sürelerde neredeyse iki kat artış (k < 1) ile ilişkilendirilmiştir. Genel eğitim orta düzeyde fayda sağlarken, gayri resmi veya hiç eğitim almayan gruplarda anlamlı bir iyileşme gözlenmemiş; bu gruplarda tehlike oranları sabit kalmış ya da artmıştır. Heinrich’in Güvenlik Piramidi ve Reason’ın İsviçre Peyniri Modeli gibi davranışsal güvenlik kuramlarına dayanan ve AIC, BIC ve olabilirlik oranı testleriyle doğrulanan bulgular, yapılandırılmış güvenlik programlarının anlamlı ve kalıcı davranış değişiklikleri sağladığını göstermektedir. Çalışma, görev odaklı ve teknoloji destekli eğitimlerin—örneğin sanal gerçeklik uygulamaları ve giyilebilir güvenlik sistemleri—karmaşık inşaat ortamlarında modern güvenlik stratejilerinin vazgeçilmez bir parçası olması gerektiğini savunmaktadır.

Anahtar Kelimeler

İnşaat Güvenliği , Güvenlik Eğitimi , Kaza Azaltma , İstatistiksel Modelleme , Sağkalım Analizi

Kaynakça

• International Labour Organization, "Global statistics on construction accidents," ILO Rep., 2023. [Online]. Available: www.ilo.org (accessed January 2, 2025).
• L. Albert and C. Routh, "Designing impactful construction safety training interventions," Safety, vol. 7, no. 2, p. 42, May 2021, 10.3390/safety7020042.
• F. Ricci, A. Chiesi, C. Bisio, C. Panari, and A. Pelosi, "Effectiveness of occupational health and safety training: A systematic review with meta-analysis," J. Workplace Learn., vol. 28, no. 6, pp. 355-377, 2016, 10.1108/JWL-11-2015-0087.
• J. Reason, Human Error. Cambridge, U.K.: Cambridge Univ. Press, 1990.
• B. H. W. Guo and Y. M. Goh, "Ontology for design of active fall protection systems," Autom. Constr., vol. 81, pp. 47-56, 2017, 10.1016/j.autcon.2017.02.009.
• L. Wu, Y. Meng, X. Kong, and Y. Zou, "Incorporating survival analysis into the safety effectiveness evaluation of treatments: Jointly modeling crash counts and time intervals between crashes," J. Transp. Saf. Secur., vol. 14, pp. 338-358, 2020, 10.1080/19439962.2020.1786871.
• K. Xie, K. Ozbay, H. Yang, and D. Yang, "A new methodology for before-after safety assessment using survival analysis and longitudinal data," Risk Anal., vol. 39, 2018, 10.1111/risa.13251.
• H. W. Heinrich, D. Petersen, and N. Roos, Industrial Accident Prevention: A Safety Management Approach, 5th ed. New York, NY, USA: McGraw-Hill, 1980.
• Z. Zhou, Y. M. Goh, and Q. Li, "Overview and analysis of safety management studies in the construction industry," Saf. Sci., vol. 72, pp. 337-350, 2015, 10.1016/j.ssci.2014.10.006.
• N. A. Duc, "Understanding the correlates of construction safety of high-rise buildings: A Bayesian perspective," J. Sci. Technol. Civil Eng., 2024, 10.31814/stce.huce2024-18(1)-06.
• A. R. Anireddy, "Construction Safety Management: Analyzing the Effectiveness of Safety Training Programs on Job Sites," Int. J. Sci. Res. Eng. Manag., 2024, 10.55041/ijsrem16079.
• H. Wang, E. M. Kamal, and N. Md Ulang, "Supervisor leadership and construction worker safety behavior: A systematic literature review," J. Southwest Jiaotong Univ., vol. 59, no. 2, p. 33, 2024, 10.35741/issn.0258-2724.59.2.33.
• A. J.-P. Tixier and M. R. Hallowell, "Safer together: Machine learning models trained on shared accident datasets predict construction injuries better than company-specific models," arXiv:2301.03567, 2023.
• A. J.-P. Tixier, M. R. Hallowell, and B. Rajagopalan, "Construction safety risk modeling and simulation," arXiv:1609.07912, 2016.
• M. Kumar, U. S. Yaligar, T. Rajpradeesh, R. K., N. Murthy, and C. Banerjee, "Identification of Key Factors Influencing Accidents on Construction Sites by Using SPSS," Int. J. Commun. Netw. Inf. Secur., vol. 16, no. 3, pp. 456-462, 2024.
• A. J. Al-Bayati, "Impact of construction safety culture and construction safety climate on safety behavior and safety motivation," Safety, vol. 7, no. 2, p. 41, 2021, 10.3390/safety7020041.
• H. F. van der Molen et al., "Interventions to prevent injuries in construction workers: An overview of systematic reviews," Amer. J. Ind. Med., vol. 55, no. 8, pp. 844-855, 2012.
• Z. Aghajani Aliabadi and S. Soltanzadeh, "Effectiveness of contractor safety training in the reduction of work-related accidents," J. Occup. Health Epidemiol., vol. 10, no. 4, pp. 372-380, 2021.
• X. Ferrada, P. Masalan, M. Guarello, and P. Nuñez, "Training workers and reducing the rate of accidents: A proposal inspired by the chronic care model," Rev. Constr., vol. 18, no. 2, pp. 364-374, 2019, 10.7764/RDLC.18.2.364.
• Y. Gao, V. A. González, and T. W. Yiu, "The effectiveness of traditional tools and computer-aided technologies for health and safety training in the construction sector: A systematic review," Comput. Educ., vol. 138, pp. 101-115, 2019, 10.1016/j.compedu.2019.05.003.
• R. M. Choudhry, D. Fang, and S. Mohamed, "Developing a model of construction safety culture," Saf. Sci., vol. 45, no. 6, pp. 1155-1168, 2007, 10.1016/j.ssci.2007.04.003.
• L. Albert and C. Routh, "Designing impactful construction safety training interventions," Safety, vol. 7, no. 2, p. 42, May 2021, 10.3390/safety7020042.
• N. Seddik-Ameur and W. Treidi, "On testing the fit of accelerated failure time and proportional hazard Weibull extension models," J. Stat. Theory Pract., vol. 12, no. 2, pp. 397-411, 2017, 10.1080/15598608.2017.1397568.
• J. Chang, S. Han, S. Abourizk, and J. Kanerva, "Stratified statistical analysis for effectiveness evaluation of frontline worker safety intervention: Case study of construction steel fabrication," Saf. Sci., 2019, 10.1016/j.ssci.2019.01.030.
• H. Lingard, T. Cooke, and N. Blismas, "Safety climate in conditions of construction subcontracting: A multi-level analysis," Constr. Manag. Econ., vol. 30, no. 2, pp. 141-156, 2013, 10.1080/01446193.2012.742627.
• S. Bilir and G. E. Gürcanli, "A method to calculate the accident probabilities in the construction industry using a Poisson distribution model," in Adv. Saf. Manage. Hum. Factors, P. Arezes, Ed. Cham, Switzerland: Springer, 2016, pp. 513-523, 10.1007/978-3-319-41929-9_47.
• J. Ma and K. M. Kockelman, "Bayesian multivariate Poisson regression for models of injury count, by severity," Transp. Res. Rec., vol. 1950, pp. 24-34, 2006, 10.3141/1950-04.
• D. I. Inouye, E. Yang, G. I. Allen, and P. Ravikumar, "A review of multivariate distributions for count data derived from the Poisson distribution," Wiley Interdiscip. Rev. Comput. Stat., vol. 9, no. 3, p. e1398, 2017, 10.1002/wics.1398.
• K. F. Sellers and G. Shmueli, "A flexible regression model for count data," Ann. Appl. Stat., vol. 4, no. 2, pp. 943-961, 2010, 10.1214/09-AOAS306.
• P. Gholizadeh and B. Esmaeili, "Developing a multi-variate logistic regression model to analyze accident scenarios: Case of electrical contractors," Int. J. Environ. Res. Public Health, vol. 17, no. 13, 2020, 10.3390/ijerph17134852.
• Z. Zhang, "Parametric regression model for survival data: Weibull regression model as an example," Ann. Transl. Med., vol. 4, no. 24, p. 484, 2016.
• E. Makalic and D. F. Schmidt, "Minimum message length inference of the Weibull distribution with complete and censored data," arXiv:2209.14587, 2022.
• J. E. Cavanaugh and A. A. Neath, "The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements," Wiley Interdiscip. Rev. Comput. Stat., vol. 11, no. 3, p. e1460, 2019.
• A. Neal and M. A. Griffin, "Safety climate and safety behaviour," Aust. J. Manag., vol. 27, no. 1_suppl, pp. 67-78, 2002, 10.1177/031289620202701s08.
• S. Bhandari et al., "Assessing the quality of safety-focused leadership engagements," Prof. Saf., vol. 67, no. 1, pp. 22-28, 2022.
• H. Lingard, T. Cooke, and N. Blismas, "Safety climate in conditions of construction subcontracting: A multi-level analysis," Constr. Manag. Econ., vol. 30, no. 2, pp. 141-156, 2013, 10.1080/01446193.2012.742627.
• A. Al-Mansouri and O. El-Sayed, "Evaluation of the effectiveness of safety training programs in construction sites," Int. J. Res. Civil Eng. Technol., vol. 5, no. 2, pp. 58-62, 2024.
• J. Reason, Managing the Risks of Organizational Accidents. Farnham, U.K.: Ashgate, 1997.
• J. Reason, Managing the Risks of Organizational Accidents. Farnham, U.K.: Ashgate, 1997.
• S. Dekker, The Field Guide to Understanding 'Human Error', 3rd ed. Farnham, U.K.: Ashgate, 2014.
• M. Niu and R. M. Leicht, "Evaluating the safety climate in construction projects: A longitudinal mixed-methods study," Buildings, vol. 14, no. 12, p. 4070, Dec. 2024, 10.3390/buildings14124070.
• X. Li et al., "A critical review of virtual and augmented reality (VR/AR) applications in construction safety," Autom. Constr., vol. 86, pp. 150-162, 2018, 10.1016/j.autcon.2017.12.002.
• A. Oğuz and O. Hansu, "Comparative Risk Management Strategies in Urban Mega-Projects: Insights into Regulatory Frameworks and Digital Tools from the United Kingdom and Türkiye," SETSCI Conference Proceedings, vol. 23, pp. 140–148, 2025, 10.36287/setsci.23.59.001.
• A. Oğuz and O. Hansu, "Circular Economy for Climate-Resilient and Sustainable Construction," Harran Üniversitesi Mühendislik Dergisi, vol. 10, no. 3, pp. 138–171, 2025, 10.46578/humder.1673599.