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Predicting index to complete schedule performance indicator in highway projects using artificial neural network model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Inaccurate estimation in highway projects represents a major problem facing planners and estimators, especially when data and information about the projects are not available, and therefore the need to use modern technologies that addresses the problem of inaccuracy of estimation arises. The current methods and techniques used to estimate earned value indexes in Iraq are weak and inefficient. In addition, there is a need to adopt new and advanced technologies to estimate earned value indexes that are fast, accurate and flexible to use. The main objective of this research is to use an advanced method known as artificial neural networks to estimate the TSPI of highway buildings. The application of artificial neural networks as a new digital technology in the construction industrial in Republic of Iraq is absolutely necessary to ensure successful project management. One model built to predict the TCSPI of highway projects. In this current study, artificial neural network model were used to model the process of estimating earned value indexes, and several cases related to the construction of artificial neural networks have been studied, including network architecture and internal factors and the extent of their impact on the performance of artificial neural network models. Easy equation was developed to calculate that TSPI. It was found that these networks have the ability to predict the TSPI of highway projects with a very outstanding saucepan of reliability (97.00%), and the accounting coefficients (R) (95.43%).
Rocznik
Strony
541--554
Opis fizyczny
Bibliogr. 16 poz., il., tab.
Twórcy
  • University of Diyala, College of Engineering, Department of Civil Engineering, Iraq
  • Erbil Polytechnic University, Erbil Technology Institute, Department of Road Construction, Erbil- Iraq
  • University of Mustansiriyah, College of Engineering, Department of Environmental Engineering, Iraq
  • Al-Nahrain University, College of Engineering, Department of Civil Engineering, Iraq
Bibliografia
  • [1] F. M. Al-Zwainy, “The Use of Artificial Neural Net Work for Estimate Total Cost of Highway Construction Projects.” Ph. D. thesis, Civil Eng. Department, Baghdad University, 2009.
  • [2] F. M. S. Alzwainy, I. A. Mohammed, and D. S. Mohsen, Earned Value Management in Construction Project. LAP LAMBERT Academic Publishing, 2015.
  • [3] F. M. S. Al-Zwainy and I. A. A. Aidan, “Forecasting the Cost of Structure of Infrastructure Projects Utilizing Artificial Neural Network Model (Highway Projects as Case Study),” Indian J. Sci. Technol., 2017, doi: 10.17485/ijst/2017/v10i20/108567.
  • [4] F. M. S. Alzwainy, R. H. Al-Suhaily, and Z. M. Saco, Project management and artificial neural networks: Fundamental and application. LAP LAMBERT Academic Publishing, 2015.
  • [5] L. Fausett, Fundamentals Of Neural Network Architectures, Algorithms, and Applications. 1994.
  • [6] T. P. William, “Neural networks to predict construction cost indexes,” Topping, BHV, Khan, AI, Ed., 2004.
  • [7] M. B. Murtaza and D. J. Fisher, “Neuromodex-neural network system for modular construction decision making,” J. Comput. Civ. Eng., 1994, doi: 10.1061/(ASCE)0887-3801(1994)8:2(221).
  • [8] T. Hegazy and O. Moselhi, “Analogy-based solution to markup estimation problem,” J. Comput. Civ. Eng., 1994, doi: 10.1061/(ASCE)0887-3801(1994)8:1(72).
  • [9] R. Lippmann, An introduction to computing with neural nets, vol. 4, no. 2. IEEE, 1987.
  • [10] D. K. H. Chua, Y. C. Kog, P. K. Loh, and E. J. Jaselskis, “Model for construction budget performance - Neural network approach,” J. Constr. Eng. Manag., 1997, doi: 10.1061/(ASCE)0733-9364(1997)123:3(214).
  • [11] H. Al-Tabtabai, N. Kartam, I. Flood, and A. P. Alex, “Expert judgment in forecasting construction project completion,” Engineering, Construction and Architectural Management. 1997, doi: 10.1108/eb021053.
  • [12] H. Adeli and M. Wu, “Regularization neural network for construction cost estimation,” J. Constr. Eng. Manag., 1998, doi: 10.1061/(asce)0733-9364(1998)124:1(18).
  • [13] T. Hegazy and A. Ayed, “Neural network model for parametric cost estimation of highway projects,” J. Constr. Eng. Manag., 1998, doi: 10.1061/(ASCE)0733-9364(1998)124:3(210).
  • [14] S. K. Zamim, N. S. Faraj, I. A. Aidan, F. M. S. Al-Zwainy, M. A. Abdul Qader, and I. A. Mohammed, “Prediction of dust storms in construction projects using intelligent artificial neural network technology,” Period. Eng. Nat. Sci., 2019, doi: 10.21533/pen.v7i4.857.
  • [15] I. A. Aidan, D. Al-Jeznawi, and F. M. S. Al-Zwainy, “Predicting Earned Value Indexes in Residential Complexes’ Construction Projects Using Artificial Neural Network Model,” vol. 13, no. 4, 2020, doi: 10.22266/ijies2020.0831.22.
  • [16] F. Kh. Jaber, N. A. Jasim, F. M. S. Al-Zwainy, “Forecasting techniques in Construction industry: Earned value indicators and performance models,” Scientific Review – Engineering and Environmental Sciences, vol. 19, no. 2, 2020.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4c761447-77e1-4417-9fb9-cc8b3b8bb315
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