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 management strategy for foreign general contractors (FGCs) who work in or plan to enter the Ethiopian construction industry. A total of 41 risk factors were identified for construction project scheduling on the basis of a literature review and pilot questionnaire survey. On the basis of the historical data of 94 construction projects, a questionnaire survey of 75 domain experts, and 3 statistical analysis methods, 22 risk factors were systematically selected from the aforementioned 41 risk factors. In the CSRAM, an Artificial Neural Network (ANN) was used to develop the inference model for the prediction of project schedule delay. The Garson algorithm (GA) was used to compute the relative weights of the 22 selected risk factors by partitioning the connection weights of the trained ANN with ranking. For comparison, the Relative Importance Index (RII) was also used 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 RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as the input of the trained ANN to conduct sensitivity analysis for predicting construction schedule delays. The findings of this study indicate that improvements in factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as a reference for FGCs who work in or plan to enter the Ethiopian construction industry to prevent or reduce construction schedule delay.

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 management strategy for foreign general contractors (FGCs) who work in or plan to enter the Ethiopian construction industry. A total of 41 risk factors were identified for construction project scheduling on the basis of a literature review and pilot questionnaire survey. On the basis of the historical data of 94 construction projects, a questionnaire survey of 75 domain experts, and 3 statistical analysis methods, 22 risk factors were systematically selected from the aforementioned 41 risk factors. In the CSRAM, an Artificial Neural Network (ANN) was used to develop the inference model for the prediction of project schedule delay. The Garson algorithm (GA) was used to compute the relative weights of the 22 selected risk factors by partitioning the connection weights of the trained ANN with ranking. For comparison, the Relative Importance Index (RII) was also used 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 RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as the input of the trained ANN to conduct sensitivity analysis for predicting construction schedule delays. The findings of this study indicate that improvements in factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as a reference for FGCs who work in or plan to enter the Ethiopian construction industry to prevent or reduce construction schedule delay.

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