Development of a Stochastic Model of Pavement Distress Initiation

Shin, Hee Cheol; Madanat, Samer

doi:10.2208/jscej.2003.744_61

Cited by 45 publications

(13 citation statements)

References 2 publications

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“…Most of the research work was primarily concerned with right censoring and incorporated a large number of explanatory variables selected by statistical tests. A number of researchers estimated the time until crack initiation using parametric (Shin andMadanat 2003, Loizos andKarlaftis 2005) or semi-parametric models (Nakat and Madanat 2008) based on data either from accelerated pavement tests (experimental data) or from condition surveys of in-service pavements (field data). Cracking-initiation models were also derived by joint estimation using both experimental and field data (Christofa andMadanat 2010, Reger et al 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

Donev

Hoffmann

2017

International Journal of Pavement Engineering

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An accurate estimation of service life is of primary interest in pavement management systems limiting the time frame for maintenance and rehabilitation (M&R) treatments. Common condition prediction models are derived by regression analysis at the road network level based on empirical data from periodic condition surveys. If a particular section has not failed prior to the last survey or the condition has improved (e.g. due to treatment), it is considered as censored. If censoring is neglected the performance functions, service lives and estimated costs may show substantial bias. The authors who acknowledge this problem have used standard statistical (survival analysis) techniques accounting for censoring. However, any road section may fail due to different but dependent competing failure causes (risks), each leading to treatments. This constitutes a special type of censoring that cannot be addressed with traditional survival analysis methods relying on the assumption of independent censoring. As the number of failure causes usually exceeds one (e.g. fatigue, permanent deformation, thermal cracking), this case is quite common. Moreover, the time until a first failure depends on the sign and degree of correlation between present failure types being modelled by the overall survival function. This paper presents a critical review and comparison of common regression, Markov chain and survival analysis models with and without correlated competing risks based on computer-generated data. Using performance history and distress progression models at the section level in combination with survival analysis improves the accuracy of predictions in comparison. Furthermore, the paper proposes a simultaneous modelling of joint and marginal service life distributions based on copula functions as generalised solution accounting for dependence between competing risks. As the focus of this paper is on condition prediction with censored data, the distress-specific planning and optimisation of treatments will be covered in forthcoming papers.

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Section: Literature Reviewmentioning

confidence: 99%

Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

Donev

Hoffmann

2017

International Journal of Pavement Engineering

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“…One of the probabilistic models used is the Markov model (Shin and Madanat 2003, Tsuda et al 2006, Nakat and Madanat 2008). The use of Markov models is also evident in research on the management of other infrastructure objects, such as bridges (Golabi and Shepard 1997, Roelfstra et al 2004, Tsuda et al 2006) and pipelines Knight 2004, Sinha andMcKim 2007).…”

Section: Modelling Deterioration and Determining Optimal Interventionmentioning

confidence: 99%

Use of exponential hidden Markov models for modelling pavement deterioration

Lethanh¹,

Adey²

2013

International Journal of Pavement Engineering

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In this paper, the potential of using an exponential hidden Markov model to model an indicator of pavement condition as a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the values of the pavement condition indices can be adequately described using discrete condition states and modelled as a Markov process. It is also assumed that the values of the indices can be measured over time and represented continuously using exponential distributions. The potential advantage of using such a model is illustrated using a real-world example.

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“…Detailed descriptions of hazard model estimation methods have been already compiled 9);10) . In the åeld of asset management for example, Shin and Madanat 11) propose a Weibull deterioration hazard model to predict the time in which road pavement cracking initiates. However, in the traditional hazard model applied to facilities and machines, the deterioration state is expressed only by two values that deåne whether a failure exists or not.…”

Section: ．Fundamentalsmentioning

confidence: 99%

Estimating Markovian Transition Probabilities for Bridge Deterioration Forecasting

Tsuda

Kaito²,

Aoki³

et al. 2006

Structural Eng./Earthquake Eng.

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In this paper, a methodology to estimate the Markovian transition probability model is presented to forecast the deterioration process of bridge components. The deterioration states of the bridge components are categorized into several ranks, and their deterioration processes are characterized by hazard models. The Markovian transition probabilities between the deterioration states which are deåned for the åxed intervals between the inspection points in time, are described by the exponential hazard models. The applicability of the estimation methodology presented in this paper is investigated by the empirical data set of steel bridges in New York city.

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Development of a Stochastic Model of Pavement Distress Initiation

Cited by 45 publications

References 2 publications

Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

Use of exponential hidden Markov models for modelling pavement deterioration

Estimating Markovian Transition Probabilities for Bridge Deterioration Forecasting

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