Using a Fuzzy Model to Evaluate Risks caused by Variation Orders in Construction Projects
Received: 30 July 2024 | Revised: 4 September 2024 | Accepted: 28 September 2024 | Online: 28 November 2024
Corresponding author: Ibrahim Mahamid
Abstract
The construction industry is the main driving sector for any country's economy. However, it is too risky due to the many risks and challenges in this industry. One of the main challenges in construction projects is variation orders, as they lead to conflicts between project parties and cause poor project performance. This study aims to evaluate the risks associated with variation orders in construction projects using fuzzy risk assessment. The targeted population is the construction contractors in Palestine. A questionnaire survey was used to collect data on the severity and frequency of the 22 identified factors of variation orders. Then, a fuzzy logic system was developed to rate these factors. The factor risk level was determined by connecting the relationship between the severity and frequency indices using If-Then rules. The determination of risk level is a critical task in the proposed fuzzy system, which depends on the complex combinations of all If-Then rules. Risk is determined not only by the If-Then rules but also by the weight of each rule. This study shows that the factors with the highest risk levels are scope change by the client, client financial problems, unavailability of required materials, poor design documents, and modification of specifications. The results can serve as guidelines for project participants who need to prepare and implement a comprehensive and effective risk management plan to meet project goals. To improve project performance, construction parties are recommended to implement the proposed method to assess risks related to construction projects. Finally, the proposed risk model demonstrates the ability to evaluate risk levels by aggregating rules and combining the project risk factors using a fuzzy logic model and MATLAB.
Keywords:
if-then, fuzzy logic, construction, variation, variation ordersDownloads
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