Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 28, Pages: 1-7
Abbas Mahde Abd1, Nidal Adnan Jasim2 and Fatima Saleh Naseef2*
1Architectural Engineering Department, College of Engineering, University of Diyala, Iraq; [email protected]
2Department of Civil Engineering, College of Engineering, University of Diyala, Iraq;
[email protected], [email protected]
*Author for correspondence
Fatima Saleh Naseef
Department of Civil Engineering, College of Engineering, University of Diyala, Iraq;
Objectives: It is very hard estimated the budget of construction projects at the first step of the building because of the limited data about the project at this step. Developing a mathematical equation to estimate the budget of the Iraqi construction project at initial step is the aim of this study. Method: It involves using of Artificial Neural Network (ANN) to develop the mathematical equation. The researchers collected the information about cost for 501 sets project for the duration (2005–2015). The total costs of 25 activity of construction work such as (excavation the foundation works, Landfill works, filling with sub-base works, Construction works under moisture proof layer, Construction works above moisture proof layer, Construction works of sections, ordinary concrete for walkways, reinforced concrete foundation, reinforced concrete column, reinforced concrete lintel, reinforced concrete slabs, reinforced concrete beams, reinforced concrete stair, reinforced concrete for the sun bumper, plaster finishing works, cement finishing works, Plastic Paints, Pentellite paints, pigment color, Stone packaging, Works of placing marble, Ceramic works for floor, Ceramic works for walls, Flattening (two opposite layers of lime), Flattening (Tiling)) are utilized for cost prediction. Findings: The results of the correlation factor equal to (100%), the percentage of error equal (5.81%) and amount of precision was (94.19%) which indicated that the artificial neural network gives very good performance in prediction construction cost. Applications: ANN is proved useful in estimating the costs of construction well in advance especially when the data are incomplete or limited.
Keywords: Artificial Neural Network, Construction Project, Cost Estimate, Cost Estimation Model
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