Cost assessment of construction projects through neural networks

Date

2013-06-01

Author

Gulcicek, Umit
Ozkan, Omer
Gunduz, Murat
DEMİR, İSMAİL HAKKI

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Construction performance parameters have experienced greater international attention and discussion in recent years. In this study, the change in the load-bearing system cost of a reinforced concrete housing estate building was investigated in relation to the building importance factor, earthquake region, soil type, floor area, and the number of stories. Three different housing estate projects with seven and fifteen stories were investigated. The structural design calculations were performed according to four different soil types, four different earthquake regions, and four different importance factors. With regards to each combination, project costs were calculated. The changes in the cost were examined with artificial neural network and multiple regression models. According to the results of the analysis, the building importance factor was the most significant component, while earthquake region was the second most significant, followed by the factors of the number of floors and the soil type.

Subject Keywords

Civil and Structural Engineering, General Environmental Science

URI

https://hdl.handle.net/11511/67845

Journal

CANADIAN JOURNAL OF CIVIL ENGINEERING

DOI

https://doi.org/10.1139/cjce-2012-0442

Collections

Department of Civil Engineering, Article

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Citation Formats

U. Gulcicek, O. Ozkan, M. Gunduz, and İ. H. DEMİR, “Cost assessment of construction projects through neural networks,” CANADIAN JOURNAL OF CIVIL ENGINEERING, pp. 574–579, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67845.