Abstract
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.
Highlights
Summarizing the issues discussed above, the main purpose of this research was to develop an integrated approach, the Construction Schedule Risk Assessment Model (CSRAM), which would reduce to a minimum the subjective judgement made by experts in risk factor identification; concurrently, the interactive relationships of these factors in the analysis process were considered in order to enhance the consistency of factor ranking
The analysis results validate whether the Garson algorithm (GA) or Relative Importance Index (RII) is more accurate in indicating the risk factors that have strong influences on construction schedule delays in Ethiopian construction projects
The schedule delay obtained from the 10% improvement of the top-ranked risk factors in the GA analysis was lower than that obtained from the 40% improvement in the top three ranked risk factors in the RII analysis
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Due to the subjective judgements of the experts, the identified factors varied from among the different research outcomes These inconsistent findings have caused project managers some difficulties in determining the right factors to consider for schedule delay reduction. Identifying and including the interactive relationships of the factors in the analysis for priority ranking will be the main concern to efficiently and significantly improve the schedule delay. Summarizing the issues discussed above, the main purpose of this research was to develop an integrated approach, the CSRAM, which would reduce to a minimum the subjective judgement made by experts in risk factor identification; concurrently, the interactive relationships of these factors in the analysis process were considered in order to enhance the consistency of factor ranking. The CSRAM includes three phases of analysis: risk factor selection and database development, factor ranking using AI, and comparison and sensitivity analysis for validation
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