A system for bridge network condition assessment and prediction

B. Samali; K. I. Crews; K. Aboura; W. Ariyaratne; P. B. Manamperi

doi:10.1201/b10571-134

A system for bridge network condition assessment and prediction

B. Samali, K. I. Crews, K. Aboura, W. Ariyaratne, P. B. Manamperi

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

Traditionally, bridge management systems were designed using a Markov chain decision model. Based on the analysis of 15 years of bridge inspection data, we apply the gamma process instead. After extracting all relevant information, enough data was collected on the condition paths of elements to build a deterioration model. The element conditions follow a time period in full condition then start deteriorating. We consider a random variable for the last time the condition was observed to be 100%. We consider the stochastic deterioration process that follows. The amalgamation of the two parts process through probabilistic arguments creates a new stochastic process. The novel stochastic process characteristics are derived through the data to provide a predictive model for the element, bridge and network conditions. We showcase a software solution for bridge network condition assessment, monitoring and prediction.

Original language	English
Title of host publication	Incorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010
Publisher	CRC Press
Pages	739-744
Number of pages	6
ISBN (Print)	9780415616577
DOIs	https://doi.org/10.1201/b10571-134
Publication status	Published - 2011
Event	Australasian Conference on the Mechanics of Structures and Materials - Duration: 11 Dec 2012 → …

Conference

Conference	Australasian Conference on the Mechanics of Structures and Materials
Period	11/12/12 → …

Keywords

bridges
maintenance and repair
structural health monitoring

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10.1201/b10571-134

Cite this

Samali, B., Crews, K. I., Aboura, K., Ariyaratne, W., & Manamperi, P. B. (2011). A system for bridge network condition assessment and prediction. In Incorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010 (pp. 739-744). CRC Press. https://doi.org/10.1201/b10571-134

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abstract = "Traditionally, bridge management systems were designed using a Markov chain decision model. Based on the analysis of 15 years of bridge inspection data, we apply the gamma process instead. After extracting all relevant information, enough data was collected on the condition paths of elements to build a deterioration model. The element conditions follow a time period in full condition then start deteriorating. We consider a random variable for the last time the condition was observed to be 100%. We consider the stochastic deterioration process that follows. The amalgamation of the two parts process through probabilistic arguments creates a new stochastic process. The novel stochastic process characteristics are derived through the data to provide a predictive model for the element, bridge and network conditions. We showcase a software solution for bridge network condition assessment, monitoring and prediction.",

keywords = "bridges, maintenance and repair, structural health monitoring",

author = "B. Samali and Crews, {K. I.} and K. Aboura and W. Ariyaratne and Manamperi, {P. B.}",

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Samali, B, Crews, KI, Aboura, K, Ariyaratne, W & Manamperi, PB 2011, A system for bridge network condition assessment and prediction. in Incorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010. CRC Press, pp. 739-744, Australasian Conference on the Mechanics of Structures and Materials, 11/12/12. https://doi.org/10.1201/b10571-134

A system for bridge network condition assessment and prediction. / Samali, B.; Crews, K. I.; Aboura, K. et al.
Incorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010. CRC Press, 2011. p. 739-744.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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AB - Traditionally, bridge management systems were designed using a Markov chain decision model. Based on the analysis of 15 years of bridge inspection data, we apply the gamma process instead. After extracting all relevant information, enough data was collected on the condition paths of elements to build a deterioration model. The element conditions follow a time period in full condition then start deteriorating. We consider a random variable for the last time the condition was observed to be 100%. We consider the stochastic deterioration process that follows. The amalgamation of the two parts process through probabilistic arguments creates a new stochastic process. The novel stochastic process characteristics are derived through the data to provide a predictive model for the element, bridge and network conditions. We showcase a software solution for bridge network condition assessment, monitoring and prediction.

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KW - maintenance and repair

KW - structural health monitoring

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:41347

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BT - Incorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010

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Samali B, Crews KI, Aboura K, Ariyaratne W, Manamperi PB. A system for bridge network condition assessment and prediction. In Incorporating Sustainable Practice in Mechanics of Structures and Materials: Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, Melbourne, Australia, 7- 10 December 2010. CRC Press. 2011. p. 739-744 doi: 10.1201/b10571-134

A system for bridge network condition assessment and prediction

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