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Implementing Proactive Building Asset Management Through Deterioration Prediction: A Case Study in Australia

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Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate (CRIOCM 2019)

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Abstract

Buildings are critical assets in most owner’s asset packages. Maintaining and rehabilitating buildings under various constraints is a crucial challenge for most owners due to the complexity of building components and the uncertainty of their deteriorations. The Markov process based probabilistic approach is an effective solution to develop an asset deterioration prediction model helping owner implementing proactive building asset management. This paper takes one Australian city council as an example to discuss the benefits of an asset deterioration prediction model to the proactive building management and explain how to calibrate and validate the Markov transition matrices from the chronologic discrete assets condition datasets. Two identical findings are that curves derived from the short-period dataset are steeper than that from the long-period dataset, and C2, C3 curves derived from the short-period dataset are not significant.

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Author information

Authors and Affiliations

  1. School of Engineering, RMIT University, Melbourne, Australia

    Wenkai Luo, Guomin Zhang, Huu Dung Tran, Sujeeva Setunge & Lei Hou

Authors
  1. Wenkai Luo
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  2. Guomin Zhang
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  3. Huu Dung Tran
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  4. Sujeeva Setunge
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  5. Lei Hou
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Corresponding author

Correspondence to Guomin Zhang .

Editor information

Editors and Affiliations

  1. School of Management Science and Real Estate, Chongqing University, Chongqing, Chong Qing, China

    Gui Ye

  2. School of Management, Guangzhou University, Guangzhou, Guangdong, China

    Hongping Yuan

  3. School of Architecture and Built Environment, University of Adelaide, Adelaide, SA, Australia

    Jian Zuo

Appendix. Transition Matrices

Appendix. Transition Matrices

Internal Wall Transition Matrix Derived from 1-year Dataset

Pij

C 1

C 2

C 3

C 4

C5

C 1

0.9751

0.0053

0.0134

0.0051

0.0011

C 2

0

1.00E−04

0.0996

0.8985

0.0018

C 3

0

0

0.0323

0.9604

0.0073

C 4

0

0

0

0.9887

0.0113

C 5

0

0

0

0

1

Flooring_Cover_Carpet Transition Matrix Derived from 1-year Dataset

Pij

C 1

C 2

C 3

C 4

C5

C 1

0.9647

0.008

0.0209

0.0042

0.0022

C 2

0

1.00E−04

0.0712

0.9262

0.0025

C 3

0

0

0.0154

0.9783

0.0063

C 4

0

0

0

0.9585

0.0415

C 5

0

0

0

0

1

Ceiling Transition Matrix Derived from 1-year Dataset

Pij

C 1

C 2

C 3

C 4

C5

C 1

0.9616

0.0164

0.0121

0.0052

0.0047

C 2

0

1.35E−02

0.04

0.9416

0.0049

C 3

0

0

0.0182

0.9705

0.0113

C 4

0

0

0

0.9881

0.0119

C 5

0

0

0

0

1

Internal Wall Transition Matrix Derived from 5-year Dataset

Pij

C 1

C 2

C 3

C 4

C5

C 1

0.997

0.0006

0.0008

0.0008

0.0008

C 2

0

0.9972

0.001

0.0009

0.0009

C 3

0

0

0.998

0.0011

0.0009

C 4

0

0

0

0.9988

0.0012

C 5

0

0

0

0

1

Flooring_Cover_Carpet Transition Matrix Derived from 5-year Dataset

Pij

C 1

C 2

C 3

C 4

C5

C 1

0.9912

0.006

0.0007

0.0009

0.0012

C 2

0

0.997

0.0008

0.0009

0.0013

C 3

0

0

0.9978

0.0009

0.0013

C 4

0

0

0

0.9987

0.0013

C 5

0

0

0

0

1

Ceiling Transition Matrix Derived from 5-year Dataset

Pij

C 1

C 2

C 3

C 4

C5

C 1

0.9969

0.0007

0.0008

0.0007

0.0009

C 2

0

0.9951

0.0009

0.0028

0.0012

C 3

0

0

0.9958

0.0028

0.0014

C 4

0

0

0

0.9984

0.0016

C 5

0

0

0

0

1

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Luo, W., Zhang, G., Tran, H.D., Setunge, S., Hou, L. (2021). Implementing Proactive Building Asset Management Through Deterioration Prediction: A Case Study in Australia. In: Ye, G., Yuan, H., Zuo, J. (eds) Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate. CRIOCM 2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-8892-1_67

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