Predicting the Performance of Highway Project Using Gray Numbers
International Conference on Advances in Construction Technology and Management, ACTM 2021
; 260:271-281, 2023.
Article
in English
| Scopus | ID: covidwho-2094484
ABSTRACT
Earned Value Analysis is a methodology used to monitor project performance in terms of time, scope and cost and also to deal with uncertain situations that come within. Uncertainty is a part of construction project and sometimes these situations can cause a great loss in the project’s success. Recently, to deal with uncertain situations a different approach has been developed to predict the project performance in a non-deterministic way, i.e., using gray interval numbers. A framework using gray interval numbers has been developed to predict the project performance and hence this study aims at using the framework to predict the performance of a real-life highway project of total duration of approximately 2 years. The analysis involves the verbal directed data from the site by the experts which were denoted as gray interval numbers. The results indicate that the project is under budget as the CPI is 1.06 and ahead of schedule as the SPI is 1.2. The results also show the worst case scenario that the project may exceed the budget as CPI is 0.83 and may run behind the schedule as SPI is 0.69. The outcomes of the study are in the form of range which provides the overall profile of the project and also helps the project team members to not always be accurate or deterministic with the outcomes. Since the construction sector was majorly hit by an uncertain event, i.e., COVID-19, this study can be very helpful in determining the performance after facing such a huge gap. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
International Conference on Advances in Construction Technology and Management, ACTM 2021
Year:
2023
Document Type:
Article
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