Welcome to the InfoSci Platform
Reference Hub3
Neural Networks and Statistical Analysis for Time and Cost Prediction Models of Urban Redevelopment Projects

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

Maria Gkovedarou, Georgios N. Aretoulis
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 16
ISSN: 1941-868X|EISSN: 1941-8698|EISBN13: 9781522512264|DOI: 10.4018/IJISSC.2017100103
Cite Article Cite Article

MLA

Gkovedarou, Maria, and Georgios N. Aretoulis. "Neural Networks and Statistical Analysis for Time and Cost Prediction Models of Urban Redevelopment Projects." IJISSC vol.8, no.4 2017: pp.37-52. http://doi.org/10.4018/IJISSC.2017100103

APA

Gkovedarou, M. & Aretoulis, G. N. (2017). Neural Networks and Statistical Analysis for Time and Cost Prediction Models of Urban Redevelopment Projects. International Journal of Information Systems and Social Change (IJISSC), 8(4), 37-52. http://doi.org/10.4018/IJISSC.2017100103

Chicago

Gkovedarou, Maria, and Georgios N. Aretoulis. "Neural Networks and Statistical Analysis for Time and Cost Prediction Models of Urban Redevelopment Projects," International Journal of Information Systems and Social Change (IJISSC) 8, no.4: 37-52. http://doi.org/10.4018/IJISSC.2017100103

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.