Neural Networks and Statistical Analysis for Time and Cost Prediction Models of Urban Redevelopment Projects

Maria Gkovedarou and Georgios N. Aretoulis
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Maria Gkovedarou: School of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki,Greece
Georgios N. Aretoulis: School of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki,Greece

International Journal of Information Systems and Social Change (IJISSC), 2017, vol. 8, issue 4, 37-52

Abstract: Over the last few years, a plethora of public works have taken place, focusing towards urban renewal, in the greater Thessaloniki district. Municipality of Thessaloniki, provided data for twelve public projects of urban renewal. Mathematical models have been proposed for cost and time prediction based on regression analysis. Furthermore, the Fast Artificial Neural Network (FANN Tool) was applied, to predict the duration and the final cost of the project, using volume of earthwork, as input variable. Both approaches could facilitate project stakeholders, to forecast the projects' final delivery date and cost and provide early warnings for any deviation from the initial budget. The results indicate that neural networks perform better than regression analysis' models, in the case of urban renewal projects.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jissc0:v:8:y:2017:i:4:p:37-52

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