Residential buildings conceptual cost estimates with the use of support vector regression
Authors:
- Michał Juszczyk
Abstract
Cost analyses, and the conceptual cost estimates among them, are of the key importance for the construction projects successes. Implementation of neural networks or machine learning methods provides broad possibilities for this specific type of cost. The aim of the paper is to present some results of the studies on the use of support vector regression as a machine learning tool for conceptual cost estimates of residential buildings. Results for three models based on support vector regression and radial basis kernel functions are introduced.
- Record ID
- CUTe38defa9a6414202a078c609f685e5f7
- Publication categories
- ; ;
- Author
- Journal series
- MATEC Web of Conferences, ISSN , e-ISSN 2261-236X, Irregular
- Issue year
- 2018
- Vol
- 196
- Pages
- [1-7]
- Article number
- 04090
- Other elements of collation
- tab.; wykr.; Bibliografia (na s.) - 6-7; Bibliografia (liczba pozycji) - 17; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 196
- Substantive notes
- Tyt. vol.: XXVII R-S-P Seminar, Theoretical Foundation of Civil Engineering (27RSP) (TFoCE 2018) Rostov-on-Don, Russia, September 17-21, 2018
- Conference
- XXVII R-S-P Seminar, Theoretical Foundation of Civil Engineering (27RSP) (TFoCE 2018) (27RSP, TFoCE 2018), 2018, 17-09-2018 - 21-09-2018, Rostov-on-Don, Rosja
- Keywords in English
- conceptual cost estimates, support vector regression, machine learning
- DOI
- DOI:10.1051/matecconf/201819604090 Opening in a new tab
- URL
- https://www.matec-conferences.org/articles/matecconf/abs/2018/55/matecconf_rsp2018_04090/matecconf_rsp2018_04090.html Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 15
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe38defa9a6414202a078c609f685e5f7/
- URN
urn:pkr-prod:CUTe38defa9a6414202a078c609f685e5f7
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.