- Tytuł:
- Forecasting techniques in construction industry: earned value indicators and performance models
- Autorzy:
-
Jaber, Firas K.
Jasim, Nidal A.
Al-Zwainy, Faiq M. - Powiązania:
- https://bibliotekanauki.pl/articles/118685.pdf
- Data publikacji:
- 2020
- Wydawca:
- Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
- Tematy:
-
Machine Learning Regression Techniques
MLRT
earned value indexes
SPI
CPI
TCPI - Opis:
- Machine Learning Regression Techniques (MLRT) as a shrewd method can be utilized in this study being exceptionally fruitful in demonstrating non-linear and the interrelationships among them in problems of construction projects such as the earned value indexes for tall buildings projects in Republic of Iraq. Three forecasting models were developed to foresee Schedule Performance Index (SPI) as first model, Cost Performance Index (CPI) as a second model, and the third model is To Complete Cost Performance Indicator (TCPI) in Bismayah New City was chosen as a case study. The methodology is mainly impacted by the deciding various components (variables) which impact on the earned value analysis, six free factors (X1: BAC, Budget at Completion; X2: AC, Actual Cost; X3: A%, Actual Percentage; X4: EV, Earned Value; X5: P%, Planning Percentage, and X6: PV, Planning Value) were self-assertively assigned and agreeably depicted for per tall buildings projects. It was found that the MLRT showed good results of estimation in terms of correlation coefficient (R) generated by MLR models for SPI and CPI and TCPI where the R were 85.5%, 89.2%, and 86.3% respectively. At long last, a result tends to be presumed that these models show a brilliant concurrence with the genuine estimations.
- Źródło:
-
Scientific Review Engineering and Environmental Sciences; 2020, 29, 2; 234-243
1732-9353 - Pojawia się w:
- Scientific Review Engineering and Environmental Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł