A Hidden Markov Model for Modeling Pavement Deterioration under Incomplete Monitoring Data
In this paper, the potential use of an exponential
hidden Markov model to model a hidden pavement deterioration
process, i.e. one that is not directly measurable, is investigated. It is
assumed that the evolution of the physical condition, which is the
hidden process, and the evolution of the values of pavement distress
indicators, can be adequately described using discrete condition states
and modeled as a Markov processes. It is also assumed that condition
data can be collected by visual inspections over time and represented
continuously using an exponential distribution. The advantage of
using such a model in decision making process is illustrated through
an empirical study using real world data.
[1] K. Kaito, K. Kazuhiko, H. Hayashi, and K. Kobayashi, "Forecasting
cracking process of the road by using the step-wide hazard model",
JSCE Journal (in Japanese), vol. 63, p386-402, 2007.
[2] Z.S. Nakat, and S. Madanat, "Stochastic Duration Modeling of
Pavement Overlay Crack Initiation", Journal of Infrastructure Systems,
vol. 14(3), p185-192, 2008.
[3] H.C. Shin, and S. Madanat, "Development of stochastic model of
pavement distress initation", Journal of Infrastructure Planning and
Management, vol. IV-61(744), p61-67, 2003.
[4] Y. Tsuda, K. Kaito, K. Aoki, and K. Kobayashi, "Estimating Markovian
transition probabilities for bridge deterioration forecasting", Journal of
Structural Engineering and Earthquake Engineering, vol. 23(2): 241-
256, 2006.
[5] K. Golabi, and R. Shepard, Pontis: "A System for Maintenance
Optimization and Improvement of US Bridge Networks", Interfaces,
vol. 27(1),p71-88, 1997.
bridge management systems and corrosion-included deterioration",
Journal of Bridge Engineering, vol. 9(3)p268-277, 2004.
[7] S.K. Sinha, and A.K. Mark, "Intelligent System for Condition
Monitoring of Underground Pipelines", Computer-Aided Civil and
Infrastructure Engineering, vol. 19(1), p42-53, 2004.
[8] S.K. Sinha, and A.M. Robert, "Probabilistic based integrated pipeline
management system", Tunneling and Underground Space Technology,
vol. 22(5-6): 543-552, 2007.
[9] M.T. Shahin, Pavement Management for Airports, Roads, and Parking
Lots, Springer, 2005.
[10] N. Lethanh, 2009. "Stochastic Optimization Methods for Infrastructure
Management with Incomplete Monitoring Data", PhD dissertation,
Graduate School of Engineering, Kyoto University, September, 2009.
[11] Y. Ephraim, and N. Merhav, "Hidden Markov processes", Information
Theory, IEEE Transactions, vol. 48(6),p1518-1569, 2002.
[12] W.D.O. Paterson, "International roughness index: relationship to other
measures of roughness and riding quality", Transportation Research
Record, vol. 1084, p49-59, 1990.
[13] W. Zucchini, and L.M. Lain, Hidden Markov Models for Time Series,
Taylor and Francis. 2009.
[14] O. Capper, E. Moulines, and T. Ryden. 2005. Inference in Hidden
Markov Models. Springer, 2005.
[15] R. Paroli, R. Giovanna, and S. Luigi, "Poisson Hidden Markov Models
for Time Series of Overdispersed Insurance Counts", Causality
Actuarial Society,: 461-472, 2000.
[16] G.J. McLachlan, and T. Krishnan, The EM Algorithm and Extensions.
New York. Wiley, 1997.
[17] L.R. Welch, "Hidden Markov Models and the Baum-Welch Algorithm",
IEEE Information Theory Society Newsletter, vol. 53(4), p10-13,2003.
[18] A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum likelihood
from incomplete data via the EM Algorithm", Journal of the Royal
Statistical Society Series B, vol. 39, No. 1, p1-38, 1977.
[19] A.H. Lisa, E. Jackson, C.E. Dougan, and S. Choi. "Models Relating
Pavement Quality Measures", Transportation Research Record: Journal
of the Transportation Research Board, vol. 1869: p119-125, 2004.
[20] D.C. Montgomery, and G.C. Runger, Applied Statistics and Probability
for Engineer, With CD. Phoenix Usa. Wiley India Pvt. Ltd, 2007.
[21] N. Lethanh, T. Nguyendinh, K. Kaito, and K. Kobayashi, "A
Benchmarking Approach to Pavement Management: Lessons from
Vietnam", Infrastructure Planning Review, vol. 26(1), p101-112, 2009.
[1] K. Kaito, K. Kazuhiko, H. Hayashi, and K. Kobayashi, "Forecasting
cracking process of the road by using the step-wide hazard model",
JSCE Journal (in Japanese), vol. 63, p386-402, 2007.
[2] Z.S. Nakat, and S. Madanat, "Stochastic Duration Modeling of
Pavement Overlay Crack Initiation", Journal of Infrastructure Systems,
vol. 14(3), p185-192, 2008.
[3] H.C. Shin, and S. Madanat, "Development of stochastic model of
pavement distress initation", Journal of Infrastructure Planning and
Management, vol. IV-61(744), p61-67, 2003.
[4] Y. Tsuda, K. Kaito, K. Aoki, and K. Kobayashi, "Estimating Markovian
transition probabilities for bridge deterioration forecasting", Journal of
Structural Engineering and Earthquake Engineering, vol. 23(2): 241-
256, 2006.
[5] K. Golabi, and R. Shepard, Pontis: "A System for Maintenance
Optimization and Improvement of US Bridge Networks", Interfaces,
vol. 27(1),p71-88, 1997.
bridge management systems and corrosion-included deterioration",
Journal of Bridge Engineering, vol. 9(3)p268-277, 2004.
[7] S.K. Sinha, and A.K. Mark, "Intelligent System for Condition
Monitoring of Underground Pipelines", Computer-Aided Civil and
Infrastructure Engineering, vol. 19(1), p42-53, 2004.
[8] S.K. Sinha, and A.M. Robert, "Probabilistic based integrated pipeline
management system", Tunneling and Underground Space Technology,
vol. 22(5-6): 543-552, 2007.
[9] M.T. Shahin, Pavement Management for Airports, Roads, and Parking
Lots, Springer, 2005.
[10] N. Lethanh, 2009. "Stochastic Optimization Methods for Infrastructure
Management with Incomplete Monitoring Data", PhD dissertation,
Graduate School of Engineering, Kyoto University, September, 2009.
[11] Y. Ephraim, and N. Merhav, "Hidden Markov processes", Information
Theory, IEEE Transactions, vol. 48(6),p1518-1569, 2002.
[12] W.D.O. Paterson, "International roughness index: relationship to other
measures of roughness and riding quality", Transportation Research
Record, vol. 1084, p49-59, 1990.
[13] W. Zucchini, and L.M. Lain, Hidden Markov Models for Time Series,
Taylor and Francis. 2009.
[14] O. Capper, E. Moulines, and T. Ryden. 2005. Inference in Hidden
Markov Models. Springer, 2005.
[15] R. Paroli, R. Giovanna, and S. Luigi, "Poisson Hidden Markov Models
for Time Series of Overdispersed Insurance Counts", Causality
Actuarial Society,: 461-472, 2000.
[16] G.J. McLachlan, and T. Krishnan, The EM Algorithm and Extensions.
New York. Wiley, 1997.
[17] L.R. Welch, "Hidden Markov Models and the Baum-Welch Algorithm",
IEEE Information Theory Society Newsletter, vol. 53(4), p10-13,2003.
[18] A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum likelihood
from incomplete data via the EM Algorithm", Journal of the Royal
Statistical Society Series B, vol. 39, No. 1, p1-38, 1977.
[19] A.H. Lisa, E. Jackson, C.E. Dougan, and S. Choi. "Models Relating
Pavement Quality Measures", Transportation Research Record: Journal
of the Transportation Research Board, vol. 1869: p119-125, 2004.
[20] D.C. Montgomery, and G.C. Runger, Applied Statistics and Probability
for Engineer, With CD. Phoenix Usa. Wiley India Pvt. Ltd, 2007.
[21] N. Lethanh, T. Nguyendinh, K. Kaito, and K. Kobayashi, "A
Benchmarking Approach to Pavement Management: Lessons from
Vietnam", Infrastructure Planning Review, vol. 26(1), p101-112, 2009.
@article{"International Journal of Architectural, Civil and Construction Sciences:63193", author = "Nam Lethanh and Bryan T. Adey", title = "A Hidden Markov Model for Modeling Pavement Deterioration under Incomplete Monitoring Data", abstract = "In this paper, the potential use of an exponential
hidden Markov model to model a hidden pavement deterioration
process, i.e. one that is not directly measurable, is investigated. It is
assumed that the evolution of the physical condition, which is the
hidden process, and the evolution of the values of pavement distress
indicators, can be adequately described using discrete condition states
and modeled as a Markov processes. It is also assumed that condition
data can be collected by visual inspections over time and represented
continuously using an exponential distribution. The advantage of
using such a model in decision making process is illustrated through
an empirical study using real world data.", keywords = "Deterioration modeling, Exponential distribution,
Hidden Markov model, Pavement management", volume = "6", number = "1", pages = "65-8", }
