Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems

Baik, Hyeon-Shik; Jeong, Hyung Seok; Abraham, Dulcy M.

doi:10.1061/(asce)0733-9496(2006)132:1(15)

Cited by 203 publications

(98 citation statements)

References 20 publications

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“…When presented in diagram, the condition shown in line could provide a better visualization and closer approximation to the true deteriorated condition. This practice was also used in other papers (Wirahadikusumah et al 2001;Micevski et al 2002, Baik et al 2006. Some predictive ranges collapsed into a point value and some observed values were outside the ranges.…”

Section: Resultsmentioning

confidence: 99%

“…This study adopted two scalar performance measures, namely, overall success rate and false negative rate derived from the confusion matrix (Tran et al 2009) and the goodness-of-fit test (Micevski et al 2002;Baik et al 2006) for assessing the performance of the developed deterioration models on a test dataset. The confusion matrix provides a comparison between three possible conditions of pipes against four possible situations that occur when comparing the predicted structural condition to the observed condition.…”

Section: Assessing Predictive Performance Of Developed Modelsmentioning

confidence: 99%

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Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Tran

Perera

2010

J. Infrastruct. Syst.

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Abstract. Stormwater pipe networks in Australia are designed to convey water from rainfall and surface runoff. They do not transport sewerage. Their structural deterioration is progressive with aging and will eventually cause pipe collapse with consequences of service interruption. Predicting structural condition of pipes provides vital information for asset management to prevent unexpected failures and to extend service life. This study focused on predicting the structural condition of stormwater pipes with two objectives. The first objective is the prediction of structural condition changes of the whole network of stormwater pipes by a Markov model at different times during their service life. This information can be used for planning annual budget and estimating the useful life of pipe assets. The second objective is the prediction of structural condition of any particular pipe by a neural network model. This knowledge is valuable in identifying pipes that are in poor condition for repair actions. A case study with CCTV inspection snapshot data was used to demonstrate the applicability of these two models.

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Section: Resultsmentioning

confidence: 99%

Section: Assessing Predictive Performance Of Developed Modelsmentioning

confidence: 99%

Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Tran

Perera

2010

J. Infrastruct. Syst.

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“…Discrete-time Markov processes have been extensively used in the context of risk, reliability and maintenance management for civil infrastructures [2,8]. The Markov property mainly characterizes this class of stochastic processes.…”

Section: Markov Chainmentioning

confidence: 99%

A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network

Torre

Yeung

Morales-Nápoles

et al. 2017

Computer aided Civil Eng

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Modelling the stochastic evolution of a largescale fleet or network generally proves to be challenging due to the large number of variables and their interactions. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behaviour of single assets, one can find significantly fewer approaches addressing a fully integrated network. An extension to the standard dynamic Bayesian network is proposed by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates which translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parametrize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgement and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate-DBN where the focus is given on two specific type of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands which are related through traffic load patterns. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information.

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“…Micevski et al, [16] successfully modelled the deterioration of storm water pipes using the Markov model utilizing the Metropolis-Hastings Algorithm (MHA), one of the Markov chain Monte Carlo methods for calibration. Baik [17] developed a Markov chain based deterioration model for wastewater systems, and its transition probabilities were computed by ordered probit model (OPM). In an integrated pavement management system application, pavement deterioration prediction was performed by applying a discrete-time Markov model [18].…”

Section: Markov Decision Processmentioning

confidence: 99%

Forecasting Deterioration of Bridge Components from Visual Inspection Data

Hasan¹,

Setunge²,

Law³

et al. 2015

IJET

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Abstract-In order to extract the optimal output in the form of good management decisions with least resources, a bridge management system or BMS in short, is an essential part for every road transport authority. In a BMS, decisions regarding frequency of maintenance, conducting repairs and rehabilitation are based on inspection data collected for the bridges by trained inspectors following a condition rating method developed by the authority. The road authorities are constantly trying to convert these condition monitoring data to a meaningful practical decision supporting tool. To address this need, a study has been conducted to forecast deterioration of reinforced concrete bridge elements using Markov process. The aim of the research work is to identify the future maintenance needs utilizing the visual inspection data. Visual inspection data has been sourced from Victoria, Australia and transition matrices have been derived using Bayesian optimisation techniques of Markov chain model to predict the future condition of bridge components. Clustering of data with respect to input parameters such as era of construction, exposure conditions, annual average daily traffic and percentage of heavy vehicles can provide an improved deterioration model for bridge Engineers. Deterioration trends for three major structural components are presented in this paper.Index Terms-Bridge deterioration, bridge management system, condition monitoring, markov chain.

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Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems

Cited by 203 publications

References 20 publications

Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network

Forecasting Deterioration of Bridge Components from Visual Inspection Data

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