K. Aas, C. Czado, A. Frigessi, and H. Bakken, Pair-copula constructions of multiple dependence, Insurance: Mathematics and Economics, vol.44, issue.2, pp.182-198, 2009.
DOI : 10.1016/j.insmatheco.2007.02.001

M. Abdel-hameed, A Gamma Wear Process, IEEE Transactions on Reliability, vol.24, issue.2, pp.24152-153
DOI : 10.1109/TR.1975.5215123

T. Attema, A. Kosgodagan-acharige, O. Morales-nápoles, and J. Maljaars, Maintenance decision model for steel bridges: a case in the Netherlands, Structure and Infrastructure Engineering, vol.1, issue.2
DOI : 10.1016/j.jcsr.2005.02.006

URL : https://hal.archives-ouvertes.fr/hal-01517056

V. Bagdonavicius and M. S. Nikulin, Estimation in degradation models with explanatory variables, Lifetime Data Analysis, vol.7, issue.1, pp.85-103, 2001.
DOI : 10.1023/A:1009629311100

H. Baik, H. Seok-jeong, and D. M. Abraham, Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems, Journal of Water Resources Planning and Management, vol.132, issue.1, pp.15-24, 2006.
DOI : 10.1061/(ASCE)0733-9496(2006)132:1(15)

A. Bauer and C. Czado, Pair-Copula Bayesian Networks, Journal of Computational and Graphical Statistics, vol.8, issue.4, pp.1248-1271
DOI : 10.2307/1912557

URL : http://arxiv.org/pdf/1211.5620

A. Bauer, Pair-copula constructions for non-Gaussian Bayesian networks, p.102, 2013.
DOI : 10.1002/cjs.10131

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.1930

T. Bedford and R. M. Cooke, Vines?a new graphical model for dependent random variables. The Annals of Statistics, pp.1031-1068

P. Boutet, F. Hild, and F. Lefebvre, Probabilistic Prediction of Fatigue Life of Cracked Parts: Linear Elastic Fracture Mechanics based Approach, Procedia Engineering, vol.66, pp.343-353, 2013.
DOI : 10.1016/j.proeng.2013.12.089

C. Eike-christian-brechmann and . Czado, COPAR-multivariate time series modeling using the copula autoregressive model Applied Stochastic Models in Business and Industry, pp.495-514

E. Castillo, A. Calviño, Z. Grande, S. Sánchez-cambronero, I. Gallego et al., A Markovian-Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors, Computer-Aided Civil and Infrastructure Engineering, vol.7, issue.4, pp.193-218
DOI : 10.1201/b13827-62

E. Castillo, Z. Grande, and A. Calviño, Bayesian Networks-Based Probabilistic Safety Analysis for Railway Lines, Computer-Aided Civil and Infrastructure Engineering, vol.30, issue.1, pp.681-700, 2016.
DOI : 10.1111/mice.12146

R. M. Cooke, Experts in uncertainty: opinion and subjective probability in science. Environmental Ethics and Science Policy Series, pp.50-64, 1991.

M. Roger, L. L. Cooke, and . Goossens, TU delft expert judgment data base. Reliability Engineering & System Safety, pp.657-674, 2008.

M. Roger, D. Cooke, and . Solomatine, EXCALIBR Integrated System for Processing Expert Judgements version 3.0, p.54, 1992.

R. Cowell, A. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter, Probabilistic networks and expert systems, Statistics for Engineering and Information Science, vol.19, p.92, 1999.

P. Dagum, A. Galper, and E. Horvitz, Dynamic Network Models for Forecasting, In Uncertainty in Artificial Intelligence, pp.41-48, 1992.
DOI : 10.1016/B978-1-4832-8287-9.50010-4

URL : http://arxiv.org/abs/1303.5396

W. Frank-darsow, B. Nguyen, and E. T. Olsen, Copulas and markov processes, Illinois Journal of Mathematics, vol.36, issue.109, pp.600-642, 1992.

Y. Deng, A. Li, and Y. Liang-ding, Analysis of monitored mass strain data 152 BIBLIOGRAPHY and fatigue assessment for steel-box-girder bridges, Gongcheng Lixue/Engineering Mechanics, issue.7, pp.3169-77, 2014.

R. Edirisinghe, S. Setunge, and G. Zhang, Markov Model???Based Building Deterioration Prediction and ISO Factor Analysis for Building Management, Journal of Management in Engineering, vol.31, issue.6
DOI : 10.1061/(ASCE)ME.1943-5479.0000359

J. Ferrándiz, E. Castillo, and P. Sanmartín, Temporal aggregation in chain graph models, Journal of Statistical Planning and Inference, vol.133, issue.1
DOI : 10.1016/j.jspi.2004.03.012

J. Foulliaron, L. Bouillaut, A. Barros, and P. Aknin, Dynamic bayesian networks for reliability analysis: from a Markovian point of view to semi-markovian approaches, IFAC-PapersOnLine, vol.48, issue.21, pp.694-700, 2015.
DOI : 10.1016/j.ifacol.2015.09.608

URL : https://hal.archives-ouvertes.fr/hal-01471360

M. Dan, P. Frangopol, and . Bocchini, Bridge network performance, maintenance and optimisation under uncertainty: accomplishments and challenges, Structure and Infrastructure Engineering, vol.8, issue.4, pp.341-356

S. Frühwirth-schnatter, Finite Mixture and Markov Switching Models, 2006.

J. L. Gross, J. Yellen, and P. Zhang, Handbook of Graph Theory
DOI : 10.1201/9780203490204

M. Hanea, D. Kurowicka, and R. M. Cooke, Hybrid Method for Quantifying and Analyzing Bayesian Belief Nets, Quality and Reliability Engineering International, vol.9, issue.6, pp.709-729, 2006.
DOI : 10.1007/978-1-4613-9655-0

A. Hanea, O. M. Napoles, and D. Ababei, Non-parametric Bayesian networks, pp.265-284
DOI : 10.1201/b16387-444

A. Ronald, J. E. Howard, and . Matheson, Influence diagrams. Decision Analysis, pp.127-143

C. Huang and A. Darwiche, Inference in belief networks: A procedural guide, International Journal of Approximate Reasoning, vol.15, issue.3, pp.225-263, 1996.
DOI : 10.1016/S0888-613X(96)00069-2

A. Mohsen, . Issa, I. Hameed, M. Shabila, and . Alhassan, Structural health monitoring systems for bridge decks and rehabilitated precast prestress concrete beams

Y. Jiang, M. Saito, C. Kumares, and . Sinha, Bridge performance prediction model using the markov chain, Transportation Research Record, vol.1180, pp.25-32, 1988.

H. Joe, Families of $m$-variate distributions with given margins and $m(m-1)/2$ bivariate dependence parameters, Institute of Mathematical Statistics Lecture Notes 154 BIBLIOGRAPHY -Monograph Series, pp.120-141, 1996.
DOI : 10.1214/lnms/1215452614

H. Joe, Dependence Modeling with Copulas, p.96, 2014.

J. Fbp, Renovation techniques for fatigue cracked orthotropic steel bridge decks, p.78, 2007.

I. Michael and . Jordan, Learning in Graphical Models, MIT PR, 1999.

J. Kallen, Markov processes for maintenance optimization of civil infrastructure in the Netherlands, p.15, 2007.

E. Këllezi and N. Webber, Numerical methods for lévy processes: Lattice methods and the density, the subordinator and the time copula, p.97, 2003.

K. Kobayashi, M. Do, and D. Han, Estimation of Markovian transition probabilities for pavement deterioration forecasting, KSCE Journal of Civil Engineering, vol.131, issue.11, pp.343-351
DOI : 10.1061/(ASCE)0733-947X(1992)118:6(820)

A. Kosgodagan, O. Morales-napoles, J. Maljaars, and W. Courage, Expert judgment in life-cycle degradation and maintenance modelling for steel bridges, Life-Cycle of Engineering Systems, p.24, 2016.
DOI : 10.1201/9781315375175-313

URL : https://hal.archives-ouvertes.fr/hal-01517169

A. Kosgodagan, O. Morales-nápoles, T. G. Yeung, W. Courage, J. Maljaars et al., A two-dimension dynamic bayesian network for large-scale degradation modelling with an application to a bridges network, p.25, 2017.

D. Kurowicka and R. M. Cooke, DISTRIBUTION-FREE CONTINUOUS BAYESIAN BELIEF NETS, In Series on Quality, Reliability and Engineering Statistics, pp.309-322
DOI : 10.1142/9789812703378_0022

D. Kurowicka and R. M. Cooke, Uncertainty Analysis with High Dimensional Dependence Modelling, p.117, 2006.
DOI : 10.1002/0470863072

D. Kurowicka and H. Joe, Dependence Modeling: Vine Copula Handbook, 2011.
DOI : 10.1142/7699

S. L. Lauritzen and D. J. Spiegelhalter, Local computations with probabilities on graphical structures and their application to expert system, Journal of the Royal Statistical Society. Series B (Methodological), vol.50, issue.2, pp.157-224, 1988.

L. Steffen, D. Lauritzen, and . Nilsson, Representing and solving decision problems with limited information, Management Science, vol.47, issue.9, pp.1235-1251, 2001.

M. Liu, D. M. Frangopol, and K. Kwon, Fatigue reliability assessment of retrofitted steel bridges integrating monitored data, Structural Safety, vol.32, issue.1, pp.77-89
DOI : 10.1016/j.strusafe.2009.08.003

M. Samer, M. G. Madanat, P. S. Karlaftis, and . Mccarthy, Probabilistic infrastructure deterioration models with panel data, J. Infrastruct. Syst, vol.33131, issue.144, pp.4-91076, 1997.

J. Maljaars and A. C. Vrouwenvelder, Probabilistic fatigue life updating accounting for inspections of multiple critical locations, International Journal of Fatigue, vol.68, pp.24-37
DOI : 10.1016/j.ijfatigue.2014.06.011

J. Maljaars, F. Van-dooren, and H. Kolstein, Fatigue assessment for deck plates in orthotropic bridge decks, Steel Construction, vol.5, issue.2, pp.93-100, 2012.
DOI : 10.1002/stco.201210011

H. Manner and O. Reznikova, A Survey on Time-Varying Copulas: Specification, Simulations, and Application, Econometric Reviews, vol.8, issue.1, pp.654-687
DOI : 10.1080/07474930600713465

S. Ma?ovi´cma?ovi´c and R. Hajdin, Modelling of bridge elements deterioration for Serbian bridge inventory, Structure and Infrastructure Engineering, vol.15, issue.15, pp.976-987
DOI : 10.1061/(ASCE)TE.1943-5436.0000018

T. Micevski, G. Kuczera, and P. Coombes, Markov Model for Storm Water Pipe Deterioration, Journal of Infrastructure Systems, vol.8, issue.2, pp.49-56, 2002.
DOI : 10.1061/(ASCE)1076-0342(2002)8:2(49)

B. T. Mirzaei, P. Adey, L. Thompson, and . Klatter, The IABMAS bridge management committee overview of existing bridge management systems, p.63, 2014.

O. Morales, D. Kurowicka, and A. Roelen, Eliciting conditional and unconditional rank correlations from conditional probabilities, Reliability Engineering & System Safety, vol.93, issue.5
DOI : 10.1016/j.ress.2007.03.020

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.484.6790

O. Morales-napoles and R. D. Steenbergen, Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data, Reliability Engineering & System Safety, vol.125, issue.47, pp.153-164, 2014.
DOI : 10.1016/j.ress.2014.01.018

O. Morales-nápoles and R. D. Steenbergen, Large-Scale Hybrid Bayesian Network for Traffic Load Modeling from Weigh-in-Motion System Data, Journal of Bridge Engineering, vol.20, issue.1
DOI : 10.1061/(ASCE)BE.1943-5592.0000636

G. Morcous and A. Hatami, Developing deterioration models for nebraska bridges, p.45, 2011.

J. Pearl, Probabilistic Reasoning in Intelligent Systems, pp.17-48, 1988.

T. Reale and A. O. Connor, Cross-Entropy as an Optimization Method for Bridge Condition Transition Probability Determination, Journal of Transportation Engineering, vol.138, issue.6, pp.741-750
DOI : 10.1061/(ASCE)TE.1943-5436.0000379

A. Guillermo, E. Riveros, and . Arredondo, Predicting future deterioration of hydraulic steel structures with Markov chain and multivariate samples of statistical distributions, Journal of Applied Mathematics, vol.2014360532, pp.1-8, 2014.

D. Ross, C. R. Shachter, and . Kenley, Gaussian influence diagrams, Management Science, vol.35, issue.5, pp.527-550

D. Nozer and . Singpurwalla, Survival in dynamic environments, Statistical Science, vol.10, issue.1, pp.86-103, 1995.

A. Sklar, Fonctions de répartition à n dimensions et leurs marges Publications de l'Institut de statistique de l, pp.229-231, 1959.

O. ?pa?ková and D. Straub, Dynamic Bayesian Network for Probabilistic Modeling of Tunnel Excavation Processes, Computer-Aided Civil and Infrastructure Engineering, vol.22, issue.10, pp.1-21, 2012.
DOI : 10.1111/j.1467-8667.2007.00497.x

D. Straub, Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks, Journal of Engineering Mechanics, vol.135, issue.10, pp.1089-1099, 2009.
DOI : 10.1061/(ASCE)EM.1943-7889.0000024

URL : http://mediatum.ub.tum.de/doc/1106440/document.pdf

H. Stensgaard-toft, J. Dalsgaard-sørensen, T. Yalamas, and J. Baussaron, Reliability assessment of welded steel details in bridges using inspection, pp.3803-3810, 2014.

N. Trifonova, A. Kenny, D. Maxwell, D. Duplisea, J. Fernandes et al., Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology, Ecological Informatics, vol.30
DOI : 10.1016/j.ecoinf.2015.10.003

A. H. Vervuurt, Percentage file op rijkswegen analyse ndw-meetgegevens april 2013 (IQ-2014-33b), p.88, 2014.

G. Weber, C. Medina-oliva, B. Simon, and . Iung, Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas, Engineering Applications of Artificial Intelligence, vol.25, issue.4, pp.671-682
DOI : 10.1016/j.engappai.2010.06.002

URL : https://hal.archives-ouvertes.fr/hal-00546144

C. Werner, T. Bedford, R. M. Cooke, A. M. Hanea, and O. Morales-nápoles, Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions, European Journal of Operational Research, vol.258, issue.3, pp.801-819, 2017.
DOI : 10.1016/j.ejor.2016.10.018