Forecasting techniques in construction industry: earned value indicators and performance models

• Autorzy: Jaber Firas K. , Jasim Nidal A. , Al-Zwainy Faiq M.

Identyfikatory

DOI

10.22630/PNIKS.2020.29.2.20

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

machine learning regression techniques; MLRT; earned value indexes; SPI; CPI; TCPI

• Wydawca: Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
• Czasopismo: Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
• Rocznik: 2020
• Tom: Vol. 29, No. 2
• Strony: 234--243
• Opis fizyczny: Bibliogr. 12 poz., tab., wykr., zdj.

Twórcy

autor

Jaber Firas K.

• Middle Technical University, Electrical Engineering Technical College

autor

Jasim Nidal A.

• University of Diyala, College of Engineering

autor

Al-Zwainy Faiq M.

• Al-Nahrain University, College of Engineering, Department of Civil Engineering, 10072 Al-Jadryia, Baghdad, Iraq

Bibliografia

• Al-Zwainy, F.M.S. & Edan, I.A. (2017). Information technology in construction project management. Amman, Jordan: Darghaidaa for Publishing [translated from Arabic].
• Al-Zwainy, F.M.S., Mohammed, I. & Mohsen, D.S. (2015). Earned value management in construction project. Saarbrűcken: LAP LAMBERT Academic Publishing.
• Bilal, M. & Oyedele, L.O. (2020). Guidelines for applied machine learning in construction industry – a case of profit margins estimation. Advanced Engineering Informatics, 43, 101013. DOI 10.1016/j.aei.2019.101013
• Chen, H.L., Chen, W.T. & Lin, Y.L. (2016). Earned value project management: improving the predictive power of planned value. International Journal of Project Management, 34(1), 22-29.
• Czemplik, A. (2014). Application of earned value method to progress control of construction projects, Procedia Engineering, 91(1), 424-428.
• Elshaer, R. (2013). Impact of sensitivity information on the prediction of project’s duration using earned schedule method. International Journal of Project Management, 31(4), 579-588.
• Granskog, A., Guttman, B. & Sjödin, E. (2016). Toward a customer-centric constructionequipment industry. McKinsey & Company. Retrieved from: https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/toward-a-customer-centric-construction-equipment-industry
• Jaber, F.K., Hachem, S.W. & Al-Zwainy, F.M. (2019). Calculating the indexes of earned value for assessment the performance of waste water treatment plant. ARPN Journal of Engineering and Applied Sciences, 14(4), 792-802.
• Khamooshi, H. & Golafshani, H. (2014), EDM: Earned Duration Management, a new approach to schedule performance management and measurement. International Journal of Project Management, 32(6), 1019-1041.
• Myers, D. (2005). Construction economics: a new approach. London: Spon Press.
• Pajares, J. & Lopez-Paredes, A. (2011). An extension of the EVM analysis for project monitoring: The Cost Control Index and the Schedule Control Index. International Journal of Project Management, 29(5), 615-621.
• Sabahi, S. & Parast, M.M. (2020). The impact of entrepreneurship orientation on project performance: a machine learning approach. International Journal of Production Economics, 107621. DOI 10.1016/j.ijpe.2020.107621

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).