PRIORITIZATION OF RISK MITIGATION STRATEGIES WITH VISUAL BASED SCENARIO ANALYSIS


Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2019

Tezin Dili: İngilizce

Öğrenci: DİLŞEN KUZUCUOĞLU

Danışman: Onur Behzat Tokdemir

Özet:

The unique and complex nature of the construction industry brings a high number of injuries in construction sites. Although the importance given to the health and safety practices increased recently, still there exists inadequacy of preventative practices along with poor risk management policies. One of the reasons that cause a high incidence of injuries is the inability to identify scenarios that create injuries effectively, which in turn makes finding optimal risk mitigation strategies difficult. So, the objective of this study is to reduce injuries and their impacts on construction sites, towards which a model is proposed that aims to prioritize risk mitigation strategies with visual-based scenario analysis. Visualization of the scenarios that create injuries is proposed for the identification and analysis of risk factors. The Bow-tie model is utilized along with the Delphi process for this purpose. While constructing the bow-tie model, the mutual information between risk factors is calculated to check interdependencies and to create a dynamicity for the model. Then, for the identification of appropriate strategies, Delphi study is performed with experts and their estimations are utilized to prioritize the strategies. Results are analyzed to specify the impacts of strategies on risk reduction. Besides the overall effect on the risk reduction, the cost of the strategies is also taken into consideration while deciding for the sequence of implementation. Eventually, the proposed model is tested by a case study for injuries that occur due to contact with a sharp object in construction sites. Results demonstrate its applicability and practicality which facilitates an easy and reliable process.