Please use this identifier to cite or link to this item: http://dspace.up.edu.ps/xmlui/handle/123456789/204
Title: Quantities Predictor Model (QPM) Based on Artificial Neural Networks works for Gaza Strip Building Contractors
Authors: Haddad, Eyad
Keywords: Construction Materials
Artificial Neural Networks (ANN)
Neurosolution
Issue Date: 1-Jun-2016
Publisher: JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, VOLUME 3, ISSUE 2, JUNE 2016
Series/Report no.: VOLUME 3, ISSUE 2, JUNE 2016;35
Abstract: The management of resources is an essential task in each construction company. The aim of this study is to develop a new technique for predicting the quantities of key construction materials ―cement, reinforced steel and aggregate‖ for building projects in Gaza Strip, through developing a model that is able to help parties involved in construction projects (owner, contractors, and others) epically contracting companies to go ahead or leave the project . This model build based on Artificial Neural Networks. In order to build this model, quantitative and qualitative techniques were utilized to identify the significant parameters for the predicting quantities of key construction materials (cement, steel, Aggregate). A database of 72 weeks was collected from the construction industry in Gaza Strip. The ANN model considered eleven significant parameters as independent input variables affected on three dependent output variable " Passing (Cement, steel, Aggregate) per ton ". Neurosolution software was used to train the models. The results of the trained models indicated that neural network reasonably succeeded in predicting the quantities of three key materials. The correlation coefficient (R) is 0.98, 0.99, 0.97 for cement, reinforced steel, aggregate respectively, indicating that; there is a good linear correlation between the actual value and the estimated neural network quantities. The performed sensitivity analysis showed that the ―open crossings‖ factor has the highest rate of influence on the total quantities of materials.
URI: http://dspace.up.edu.ps/xmlui/handle/123456789/204
ISSN: 2312-2307
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