Risk analysis of construction of administration projects using Bayesian networks. Issue 1 (9th August 2021) - Osama, Mohammad; Sherif, Aly; Badawy, Mohamed
Abstract : Purpose: This paper aims to enlighten the importance of the risk management process which is considered as a major procedure to effectively handle the potential inherent risks in the construction industry. However, most traditional risk analysis techniques are based on theories that deal with each risk factor as an independent, which does not take into consideration the causal relationships between risk factors. Design/methodology/approach: This study aspires to identify the overall risk of the administrative construction projects in Egypt and to recognize the most influencing risk factors through the project life cycle by using Bayesian belief networks (BBN). Through a review of the literature, 27 risk factors were identified and categorized as the most common risk factors in the construction industry. A structured questionnaire was performed to estimate the probability and severity of these risks. Through site visits and interviews with experts in the construction field, 200 valid questionnaires were collected. A risk analysis model was developed using BBNs, then the applicability of this model was verified using a case study in Egypt. Findings: However, the outcome showed that critical risks that manipulate administrative construction projects in Egypt were corruption and bribery, contractor financial difficulties, force majeure, damage to the structure and defective material installation. Practical implications: The proposed study presents the possibilities available to the project parties to obtain a better forecast of the project objectives, including the project duration, total project cost and the target quality by examining the causal relationships between project risks and project objectives. Originality/value: This study aspires to identify the overall risk of the administrative construction projects in Egypt and to recognize the most influencing risk factors through the project life cycle by using BBNs.