Use of exponential hidden Markov models for modelling pavement deterioration

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.

[1]  William D O Paterson.  INTERNATIONAL ROUGHNESS INDEX: RELATIONSHIP TO OTHER MEASURES OF ROUGHNESS AND RIDING QUALITY , 1986 .

[2]  Samer Madanat,et al.  A bottom-up solution for the multi-facility optimal pavement resurfacing problem , 2011 .

[3]  António Pais Antunes,et al.  PROBABILISTIC SEGMENT-LINKED PAVEMENT MANAGEMENT OPTIMIZATION MODEL , 2002 .

[4]  Sunil K. Sinha,et al.  Intelligent System for Condition Monitoring of Underground Pipelines , 2004 .

[5]  E. S. Folias,et al.  On the fracture of highway pavements , 1975 .

[6]  Weng Tat Chan,et al.  Multiobjective Optimization for Pavement Maintenance Programming , 2000 .

[7]  Melvin Alexander.  Applied Statistics and Probability for Engineers , 1995 .

[8]  António Pais Antunes,et al.  A Segment-linked Optimization Model for Deterministic Pavement Management Systems , 2002 .

[9]  Sunil K. Sinha,et al.  Probabilistic based integrated pipeline management system , 2007 .

[10]  W D Paterson,et al.  Quantifying the effectiveness of pavement maintenance and rehabilitation , 1990 .

[11]  Kiyoyuki Kaito,et al.  ESTIMATING MARKOVIAN TRANSITION PROBABILITIES FOR BRIDGE DETERIORATION FORECASTING , 2005 .

[12]  Kamal Golabi,et al.  Pontis: A System for Maintenance Optimization and Improvement of US Bridge Networks , 1997 .

[13]  Y. Ouyang,et al.  Optimal scheduling of rehabilitation activities for multiple pavement facilities: exact and approximate solutions , 2004 .

[14]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[15]  M. Y. Shahin,et al.  Pavement Management for Airports, Roads, and Parking Lots , 2006 .

[16]  Bryan T. Adey,et al.  Condition Evolution in Bridge Management Systems and Corrosion-Induced Deterioration , 2004 .

[17]  Khaled A. Abaza,et al.  Pavement Rehabilitation Project Ranking Approach Using Probabilistic Long-Term Performance Indicators , 2010 .

[18]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[19]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[20]  Jorge A Prozzi,et al.  Estimation of Pavement Performance Deterioration Using Bayesian Approach , 2006 .

[21]  Samer Madanat,et al.  DEVELOPMENT OF A STOCHASTIC MODEL OF PAVEMENT DISTRESS INITIATION , 2003 .

[22]  Neri Merhav,et al.  Hidden Markov processes , 2002, IEEE Trans. Inf. Theory.

[23]  Yanfeng Ouyang,et al.  An analytical solution for the finite-horizon pavement resurfacing planning problem , 2006 .

[24]  Samer Madanat,et al.  Stochastic Duration Modeling of Pavement Overlay Crack Initiation , 2008 .

[25]  Kiyoshi Kobayashi,et al.  A BENCHMARKING APPROACH TO PAVEMENT MANAGEMENT: LESSONS FROM VIETNAM , 2009 .

[26]  Le Thanh Nam,et al.  Stochastic optimization methods for infrastructure management with incomplete monitoring data , 2009 .

[27]  Roberta Paroli,et al.  Poisson Hidden Markov models for time series of overdispersed insurance counts , 2000 .