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FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS

Construction projects, especially tunnel projects in the Arab world, suffer from poor documentation of data and information, and therefore there is a difficulty in estimating the budget or total costs or indicators of earned value, and with the advancement of artificial neural networks, the urgent n...

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Main Authors: Hammoody O., Al-Somaydaii J.A., Al-Zwainy F.M.S., Hayder G.
Other Authors: 57742664500
Format: Article
Published: ASV Publishing House 2023
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spelling my.uniten.dspace-269362023-05-29T17:37:57Z FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS Hammoody O. Al-Somaydaii J.A. Al-Zwainy F.M.S. Hayder G. 57742664500 57202395424 55347693000 56239664100 Construction projects, especially tunnel projects in the Arab world, suffer from poor documentation of data and information, and therefore there is a difficulty in estimating the budget or total costs or indicators of earned value, and with the advancement of artificial neural networks, the urgent need arises to estimate the earned value indicators for tunnels projects in the absence or lack of data required for the purpose of estimating costs and durations together. Objective: The primary aim of the current study is to introduce Artificial Intelligence (AI) in conducting statistical approach for earned value management of the tunnels projects. Methodology: The study was based on the assurance of different variables that effect on the Earned Value Management (EVM) of the tunnels projects that involves historical data in Iraq and Jordan. Five independent variables were randomly selected (Actual Cost AC, Planning Value PV, Earned Value EV, Actual Duration AD and Planning Duration PD), which were all around characterized for each tunnel project, and one dependent variable Cost Performance Index (CPI) was selected. NEUFRAME V.5 Program was selected, which is the premier neural network simulation environment. The methodology of ANN embraced for finding best network architecture and inside parameters that control and monitoring the preparation procedure which did by utilizing the default parameters of the NEUFRAME programming package. Results: The experimentation results reveal that, Mean Absolut Percentage Error (MAPE) and Average Accuracy percentage (AA) generated by ANN model (CPI) were found to be 9.6% and 90.368%, respectively. Therefore, the ANN model (CPI.model.1) shows a magnificent concurrence with the real estimations. � 2022, ASV Publishing House. All rights reserved. Final 2023-05-29T09:37:57Z 2023-05-29T09:37:57Z 2022 Article 10.22337/2587-9618-2022-18-1-51-60 2-s2.0-85132008839 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132008839&doi=10.22337%2f2587-9618-2022-18-1-51-60&partnerID=40&md5=eef377ac0fc39a1462f9499409729075 https://irepository.uniten.edu.my/handle/123456789/26936 18 1 51 60 All Open Access, Gold ASV Publishing House Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Construction projects, especially tunnel projects in the Arab world, suffer from poor documentation of data and information, and therefore there is a difficulty in estimating the budget or total costs or indicators of earned value, and with the advancement of artificial neural networks, the urgent need arises to estimate the earned value indicators for tunnels projects in the absence or lack of data required for the purpose of estimating costs and durations together. Objective: The primary aim of the current study is to introduce Artificial Intelligence (AI) in conducting statistical approach for earned value management of the tunnels projects. Methodology: The study was based on the assurance of different variables that effect on the Earned Value Management (EVM) of the tunnels projects that involves historical data in Iraq and Jordan. Five independent variables were randomly selected (Actual Cost AC, Planning Value PV, Earned Value EV, Actual Duration AD and Planning Duration PD), which were all around characterized for each tunnel project, and one dependent variable Cost Performance Index (CPI) was selected. NEUFRAME V.5 Program was selected, which is the premier neural network simulation environment. The methodology of ANN embraced for finding best network architecture and inside parameters that control and monitoring the preparation procedure which did by utilizing the default parameters of the NEUFRAME programming package. Results: The experimentation results reveal that, Mean Absolut Percentage Error (MAPE) and Average Accuracy percentage (AA) generated by ANN model (CPI) were found to be 9.6% and 90.368%, respectively. Therefore, the ANN model (CPI.model.1) shows a magnificent concurrence with the real estimations. � 2022, ASV Publishing House. All rights reserved.
author2 57742664500
author_facet 57742664500
Hammoody O.
Al-Somaydaii J.A.
Al-Zwainy F.M.S.
Hayder G.
format Article
author Hammoody O.
Al-Somaydaii J.A.
Al-Zwainy F.M.S.
Hayder G.
spellingShingle Hammoody O.
Al-Somaydaii J.A.
Al-Zwainy F.M.S.
Hayder G.
FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS
author_sort Hammoody O.
title FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS
title_short FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS
title_full FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS
title_fullStr FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS
title_full_unstemmed FORECASTING AND DETERMINING OF COST PERFORMANCE INDEX OF TUNNELS PROJECTS USING ARTIFICIAL NEURAL NETWORKS
title_sort forecasting and determining of cost performance index of tunnels projects using artificial neural networks
publisher ASV Publishing House
publishDate 2023
_version_ 1806427526209208320
score 13.211869

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