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Data mining techniques for the assessment of factors contributing to the damage of residential houses in Australia

Osman-Schlegel, N. Y., Krezel, Z. A. and McManus, K. J. 2011, Data mining techniques for the assessment of factors contributing to the damage of residential houses in Australia, in CSC 2011 : Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Civil-Comp Press, Stirlingshire, Scotland, pp. 1-12.

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Title Data mining techniques for the assessment of factors contributing to the damage of residential houses in Australia
Author(s) Osman-Schlegel, N. Y.
Krezel, Z. A.ORCID iD for Krezel, Z. A. orcid.org/0000-0002-3825-6189
McManus, K. J.
Conference name Soft Computing Technology in Civil, Structural and Environmental Engineering. Conference (2nd : 2011 Crete, Greece)
Conference location Crete, Greece
Conference dates 6-9 Sep. 2011
Title of proceedings CSC 2011 : Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering
Editor(s) Tsompanakis, Y.
Topping, B. H. V.
Publication date 2011
Conference series Soft Computing Technology in Civil, Structural and Environmental Engineering. Conference
Start page 1
End page 12
Total pages 12
Publisher Civil-Comp Press
Place of publication Stirlingshire, Scotland
Keyword(s) data mining
chi-square test
categorical regression
artificial intelligence
databases
Summary This paper reports on the preparation and management processes of inconsistent data on damage on residential houses in Victoria, Australia. There are no existing specific and fully relevant databases readily available except for the incomplete paper-based and electronic-based reports. Therefore, the extracting of information from the reports is complicated and time consuming in order to extract and include all the necessary information needed for analysis of damage on residential houses founded on expansive soils. Data mining is adopted to develop a database. Statistical methods and Artificial Intelligence methods are used to quantify the quality of data. The paper concludes that the development of such database could enable BHC to evaluate the usefulness of the reports prepared on the reported damage properties for further analysis.
Notes
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ISBN 9781905088492
ISSN 1759-3433
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 961499 Soils not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2011, Civil-Comp Press
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30042310

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