Deterioration prediction of superstructure elements of community buildings in Australia using a probabilistic approach

Mohseni, H; Setunge, S; Zhang, G; Kalutara, P

Deterioration prediction of superstructure elements of community buildings in Australia using a probabilistic approach

Mohseni, H Setunge, S Zhang, G

Kalutara, P

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Publication Type:: Chapter
Citation:: Lecture Notes in Mechanical Engineering, 2014, 9 pp. 689 - 698
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Mohseni, H	en_US
dc.contributor.author	Setunge, S	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Kalutara, P	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Mechanical Engineering, 2014, 9 pp. 689 - 698	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121472
dc.description.abstract	© Springer-Verlag London 2014. Buildings are complex infrastructure assets and optimization of maintenance and rehabilitation actions require a well-considered asset management model. Community Buildings as one of the major investments of the local government in Australia have a large proportion of demand, expectation, and consideration among the local council’s assets. The following paper introduces a building asset management (BAM) framework and a building element hierarchy which facilitates the building asset management and inspection strategies. Data gathering and preparation will be discussed followed by the calibration of a probabilistic deterioration prediction approach based on the Markov process. The Markov transition matrices have been derived for building elements based on the condition data sourced from local councils. The transition matrices for superstructure elements are presented, reviewed, and compared.	en_US
dc.relation.ispartof	Lecture Notes in Mechanical Engineering	en_US
dc.relation.isbasedon	10.1007/978-1-4471-4993-4_59	en_US
dc.title	Deterioration prediction of superstructure elements of community buildings in Australia using a probabilistic approach	en_US
dc.type	Chapter
utslib.citation.volume	9	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© Springer-Verlag London 2014. Buildings are complex infrastructure assets and optimization of maintenance and rehabilitation actions require a well-considered asset management model. Community Buildings as one of the major investments of the local government in Australia have a large proportion of demand, expectation, and consideration among the local council’s assets. The following paper introduces a building asset management (BAM) framework and a building element hierarchy which facilitates the building asset management and inspection strategies. Data gathering and preparation will be discussed followed by the calibration of a probabilistic deterioration prediction approach based on the Markov process. The Markov transition matrices have been derived for building elements based on the condition data sourced from local councils. The transition matrices for superstructure elements are presented, reviewed, and compared.

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http://hdl.handle.net/10453/121472