Construction project schedule delay is a worldwide concern and especially severe in the Ethiopian construction industry. This study developed a Construction Schedule Risk Assessment Model (CSRAM) and a management strategy for foreign general contractors (FGCs). 94 construction projects with schedule delay were collected and a questionnaire survey of 75 domain experts was conducted to systematically select 22 risk factors. In CSRAM, the artificial neural network (ANN) inference model was developed to predict the project schedule delay. Integrating it with the Garson algorithm (GA), the relative weights of risk factors with rankings were calculated and identified. For comparison, the Relative Importance Index (RII) method was also applied to rank the risk factors. Management strategies were developed to improve the three highest-ranked factors identified using the GA (change order, corruption/bribery, and delay in payment), and the RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as inputs for the trained ANN to conduct a sensitivity analysis. The findings of this study indicate that improvements in the factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as an important reference for FGCs who plan to enter or work in the Ethiopian construction industry.
Study on schedule risk assessment of power transmission and transformation project based on improved risk chain
