Predicting house damage class using artificial intelligence method Osman-Schlegel, N. Y. 2013, Predicting house damage class using artificial intelligence method, in HKICEAS 2013 : Proceedings of the 2013 Engineering and Applied Science Hong Kong International Conference, [Conference], [Hong Kong, China], pp. 486-491. Attached Files Name Description MIMEType Size Downloads osmanschlegel-predictinghouse-2013.pdf Published version application/pdf 519.09KB 138 Title Predicting house damage class using artificial intelligence method Author(s) Osman-Schlegel, N. Y. Conference name Engineering and Applied Science. Hong Kong International Conference (2nd : 2013 : Hong Kong, China) Conference location Hong Kong, China Conference dates 19 - 21 Dec. 2013 Title of proceedings HKICEAS 2013 : Proceedings of the 2013 Engineering and Applied Science Hong Kong International Conference Editor(s) [Unknown] Publication date 2013 Conference series Engineering and Applied Science Hong Kong International Conference Start page 486 End page 491 Total pages 6 Publisher [Conference] Place of publication [Hong Kong, China] Keyword(s) house damage artificial intelligence light structures damage class structural movements Summary The unsatisfactory performance of light structures founded on expansive soils subject to seasonal movements is frequently reported since the early 1950's in Australia. Excessive movements have caused damage to numerous structures that have not been adequately designed to accommodate soil volume changes. However, the sole presence of expansive soil is not necessarily the main cause of damage. Other factors such as vegetation, climate factors, types of construction materials and geology type may also contribute. This paper presents a model which predicts the damage class by analyzing combinations of the contributing factors using artificial intelligence methods. This model can help to identify if any serious and urgent repairs are necessary and immediate actions could be initiated without delay. ISBN 9789868741744 Language eng Field of Research 129999 Built Environment and Design not elsewhere classified Socio Economic Objective 970112 Expanding Knowledge in Built Environment and Design HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, The Authors Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30062600 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Architecture and Built Environment Open Access Collection   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 28 Apr 2014, 09:52:56 EST Tue, 29 Apr 2014, 08:11:10 EST Thu, 01 May 2014, 18:20:38 EST Fri, 02 May 2014, 09:34:31 EST Mon, 26 May 2014, 11:43:22 EST Tue, 27 May 2014, 09:55:15 EST Wed, 06 Dec 2017, 06:05:48 EST Fri, 07 Sep 2018, 17:04:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 311 Abstract Views, 140 File Downloads  -  Detailed Statistics Created: Mon, 28 Apr 2014, 09:52:56 EST