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| dc.contributor.author | Savas Bayram; Department of Civil Engineering, Erciyes University | |
| dc.contributor.author | Mehmet Emin Ocal; Department of Civil Engineering, Cukurova University | |
| dc.contributor.author | Emel Laptali Oral; 3Department of Civil Engineering, Cukurova University | |
| dc.date | 2013-06-14 08:54:00 | |
| dc.date.accessioned | 2013-07-15T11:48:48Z | |
| dc.date.accessioned | 2015-11-23T16:10:14Z | |
| dc.date.available | 2013-07-15T11:48:48Z | |
| dc.date.available | 2015-11-23T16:10:14Z | |
| dc.date.issued | 2013-07-15 | |
| dc.identifier | http://ecs.epoka.edu.al/index.php/iscce/iscce2012/paper/view/535 | |
| dc.identifier.uri | http://dspace.epoka.edu.al/handle/1/655 | |
| dc.description.abstract | Realistic estimation of construction cost is a vital issue for both successful planningand completion of every construction project. However, fluctuations in input prices due to the unexpected changes in factors like inflation and supply/demand balance make realistic costestimation very difficult to achieve. Thus, various estimation methods have been developedand these can be grouped as methods based on; statistics-probability analysis, comparison with similar projects and artificial intelligence techniques.Statistics-probability analysis is the most widely used method for construction costestimation in Turkey. Based on the so called method, Ministry of the Environment and Urbanism publishes and updates "Unit Costs of Construction" every year and the data is widely used for preliminary cost estimation by both the contractors and the developers.Meanwhile, methods based on artificial intelligence techniques are rarely used within the industry. Thus, the aim of this study has been to compare the estimation results obtained by using statistics-probability analysis and artificial intelligent techniques. In order to achieve this, construction cost data from 198 projects; completed between 2004-2010 in Izmir (the third largest city in Turkey) were used. Multi layer perceptron (MLP) and grid partitioning algorithm (GPA) were used to obtain estimation results and root mean square error (RMSE)and coefficient of determination (R2) were calculated for comparisons. | |
| dc.format | application/pdf | |
| dc.language | en | |
| dc.publisher | International Student Conference of Civil Engineering | |
| dc.rights | Authors who submit to this conference agree to the following terms:<br /> <strong>a)</strong> Authors retain copyright over their work, while allowing the conference to place this unpublished work under a <a href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution License</a>, which allows others to freely access, use, and share the work, with an acknowledgement of the work's authorship and its initial presentation at this conference.<br /> <strong>b)</strong> Authors are able to waive the terms of the CC license and enter into separate, additional contractual arrangements for the non-exclusive distribution and subsequent publication of this work (e.g., publish a revised version in a journal, post it to an institutional repository or publish it in a book), with an acknowledgement of its initial presentation at this conference.<br /> <strong>c)</strong> In addition, authors are encouraged to post and share their work online (e.g., in institutional repositories or on their website) at any point before and after the conference. | |
| dc.source | International Student Conference of Civil Engineering; International Student Conference of Civil Engineering | |
| dc.subject | Project preliminary cost; project schedule; cost variance; multi layer perceptron; grid partitioning algorithm | |
| dc.title | Analysis of Cost and Schedule Variances in Construction Works with Artificial Intelligence Approaches: The Case of Turkey | |
| dc.type | Peer-reviewed Paper |
