Deterioration Prediction of Timber Bridge Elements Using the Markov Chain

Ranjith, S. Kumar; Setunge, Sujeeva; Gravina, Rebecca; Venkatesan, Srikanth

doi:10.1061/(asce)cf.1943-5509.0000311

Cited by 85 publications

(48 citation statements)

References 7 publications

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“…Markov chains have been used for infrastructure deterioration predictions of bridges (Fu and Debraj, 2008;Ranjith et al, 2011), roads (Abaza and Ashur, 1999;Hudson et al, 1998;Lethanh and Adey, 2012;Li, 1997;Li and Haas, 1998;Madanat et al, 1995;Ortiz-Garcia et al, 2006;Panthi, 2009;Tack and Chou, 2002;Wang et al, 1994), waste water (Hyeon-shik et al, 2006), rail (Ferreira and Murray, 1997;Shafahi and Hakhamaneshi, 2009) and pipelines (Sinha and Mark, 2004). …”

Section: Probabilistic Modelsmentioning

confidence: 99%

Developing a new road deterioration model incorporating flooding

Khan

Mesbah

Ferreira

et al. 2014

Proceedings of the Institution of Civil Engineers - Transport

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Currently, there is no road deterioration (RD) model that can address pavement performance with flooding.Therefore, the aim of this work was to develop an RD model that considers flooding and pre-and post-flood strategies. The case study is road group(s) in Queensland, Australia, based on the 34 000 km road database of its main roads authority, as Queensland was severely affected by floods in 2011. This paper provides a brief literature review and proposes an approach to develop an RD model based on a non-homogeneous Markov transition probability matrix. The new RD models are developed for a number of road groups based on roughness and rutting data collected since 2000. Detailed results on the derived roughness-based and rutting-based RD models with flooding for two selected road groups are highlighted. The new RD models show significant pavement deterioration at different probabilities of flooding events, especially at higher probabilities. It is believed that these results will help road authorities in managing flood-damaged roads with appropriate treatments and optimum budget.

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Section: Probabilistic Modelsmentioning

confidence: 99%

Developing a new road deterioration model incorporating flooding

Khan

Mesbah

Ferreira

et al. 2014

Proceedings of the Institution of Civil Engineers - Transport

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“…In this research, joint probability method was used for identifying the most dominant relationship between bridge components. On the other hand, Markov Chain was used for predicting the probability of bridge condition [5]. The two methods were integrated with System Dynamic (SD).…”

Section: Introductionmentioning

confidence: 99%

Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic

2017

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Abstract. Instantaneous bridge failure tends to increase in Indonesia. To mitigate this condition, Indonesia's Bridge Management System (I-BMS) has been applied to continuously monitor the condition of bridges. However, I-BMS only implements visual inspection for maintenance priority of the bridge structure component instead of bridge structure system. This paper proposes a new bridge failure prediction model based on hybrid Markov-System Dynamic (MSD). System dynamic is used to represent the correlation among bridge structure components while Markov chain is used to calculate temporal probability of the bridge failure. Around 235 data of bridges in Indonesia were collected from Directorate of Bridge the Ministry of Public Works and Housing for calculating transition probability of the model. To validate the model, a medium span concrete bridge was used as a case study. The result shows that the proposed model can accurately predict the bridge condition. Besides predicting the probability of the bridge failure, this model can also be used as an early warning system for bridge monitoring activity.

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“…The deterministic models provide specific values on deterioration; whereas, the probabilistic approach relies on probable changes in condition states. Several studies have mentioned the importance of pavement deterioration prediction, and have discussed deterministic and probabilistic models for pavement performances assessment (Chun, et al 2012;Li, et al 1995;Li, et al 1996;Li, 2005;Martin, 2009;Prozzi, 2001;Ranjith, et al 2011;Robinson, et al 1998;Tack and Chou, 2002). Moreover, Austroads (2009b) and Panthi (2009) elaborated the RD modelling methods, and identified assumptions and limitations for different pavement performance models.…”

Section: Rd Modelsmentioning

confidence: 99%

“…Ranjith, et al (2011) used Australian data from the VicRoads and two regional councils of Victoria, to develop probabilistic deterioration models for timber bridges. Li (2005) suggested to consider stochastic and dynamic modelling for pavement deterioration predictions.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

“…The Markov chain has been used for the infrastructure deterioration predictions, including bridges (Fu and Debraj, 2008;Mishalani and Madanat, 2002;Ranjith, et al 2011), roads (Abaza and Ashur, 1999;Anderson, et al 1994;Butt, et al 1994;Hudson, et al 1998;Jin, et al 2010;Kobayashi, et al 2010;Lethanh and Adey, 2012;Li, 1997;Mandiartha, 2010;Mills, 2010;Ortiz-Garcia, et al 2006;Panthi, 2009;Pierce, 2003;Robinson, et al 1998;Tack and Chou, 2002;Wang, et al 1994), waste water (Hyeon-shik, et al 2006), rail (Ferreira and Murray, 1997;Shafahi and Hakhamaneshi, 2009) and pipelines (Jin, et al 2010;Sinha and Mark, 2004). …”

Section: Markov Chain Reviewmentioning

confidence: 99%

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Improvement of a Pavement Management System Incorporating Flooding

Khan¹

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Natural disasters can cause severe damage to infrastructure such as the road network. Currently, a Pavement Management System (PMS) does not incorporate flooding in a LifeCycle Analysis (LCA). A few Road Deterioration (RD) models have addressed flooding, but they have limitations. As a result, there are not any comprehensive RD models that can incorporate flooding in pavement performances. In addition, no optimum life-cycle road maintenance strategy is available. No study investigated the pavement performances because of a loss in Modulus of Resilience (Mr) at granular and subgrade layers during extreme moisture intrusion. The derivation of pre-and post-flood road maintenance strategies and a flood risk assessment should also be incorporated in a PMS.As a case study, this research has considered the January 2011 flood of Queensland, Australia, and has used 34,000 km road database of the Queensland's main roads authority.The major objectives of this study are to derive: i) network and project level roughness and rutting-based RD models with flooding, ii) pavement performances due to Mr loss at granular and subgrade layers, iii) flood-resilient pavements, iv) optimum road maintenance strategies at without flood, pre-and post-flood scenarios, and v) pavements' flood risks. The current scope covered the pavements that are affected after a flood, but are not washed away completely and need rehabilitation for structural strengthening.This research has used a probabilistic approach for deriving the RD models, which are valid at both network and project levels. Moreover, the proposed RD models can estimate road deterioration after a flood at different probabilities of flooding.The actual roughness and rutting vs. time data are assessed for the representative road groups or site specific roads to get the transition probability matrices for with and without flooding conditions, which are used in a Monte Carlo simulation. The new RD models show significant pavement deterioration at different probabilities of flooding events. The results are found valid with actual data for about 2 to 3 years after the January 2011 flood. A t-test also supports this match. A pavement's performance due to Mr losses at granular layers is checked using the two renowned roughness models, which results are found close match with the actual after flood data and RD models.

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Deterioration Prediction of Timber Bridge Elements Using the Markov Chain

Cited by 85 publications

References 7 publications

Developing a new road deterioration model incorporating flooding

Developing a new road deterioration model incorporating flooding

Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic

Improvement of a Pavement Management System Incorporating Flooding

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