Semi-Markov Models for the Deterioration of Bridge Elements

AbstractMany bridge management systems (BMSs) use a Markov chain model to forecast the deterioration process. The Markov property may be considered to be restrictive when modeling the deterioration of transportation assets, primarily because of the memoryless property and assumption of exponential distribution for sojourn times in the condition states. This study addresses some of the limitations that arise from the use of purely Markov chain deterioration models for transportation infrastructure by introducing alternative approaches that are based on the semi-Markov process. Two semi-Markov approaches for modeling the deterioration of certain bridge elements are developed. These are compared against a previously developed semi-Markov approach, the traditional Markov chain deterioration approach, and the change in the average actual condition indices of bridge elements that deteriorated for 8 years after being constructed. The results obtained from this study indicated that semi-Markov models are feasible...

[1]  Ronald A. Howard,et al.  Dynamic Probabilistic Systems , 1971 .

[2]  Andrew T. Brint,et al.  Comparing Probabilistic Methods for the Asset Management of Distributed Items , 2005 .

[3]  Nikolaos Limnios,et al.  Semi-Markov Processes , 2001 .

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

[5]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[6]  Kenneth David Kuhn Uncertainty in Infrastructure Deterioration Modeling and Robust Maintenance Policies for Markovian Management Systems , 2006 .

[7]  Andrew T. Brint,et al.  A semi-Markov approach for modelling asset deterioration , 2005, J. Oper. Res. Soc..

[8]  Samer Madanat,et al.  OPTIMAL MAINTENANCE AND REPAIR POLICIES IN INFRASTRUCTURE MANAGEMENT UNDER UNCERTAIN FACILITY DETERIORATION RATES: AN ADAPTIVE CONTROL APPROACH , 2002 .

[9]  Alessandro Birolini Reliability Engineering: Theory and Practice , 1999 .

[10]  W. Perraudin,et al.  The Estimation of Transition Matrices for Sovereign Credit Ratings , 2002 .

[11]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[12]  Oliver C. Ibe,et al.  Markov processes for stochastic modeling , 2008 .

[13]  Samer Madanat,et al.  History-Dependent Bridge Deck Maintenance and Replacement Optimization with Markov Decision Processes , 2007 .

[14]  John O Sobanjo,et al.  State transition probabilities in bridge deterioration based on Weibull sojourn times , 2011 .

[15]  R W Shepard,et al.  CALIFORNIA BRIDGE HEALTH INDEX: A DIAGNOSTIC TOOL TO MAXIMIZE BRIDGE LONGEVITY, INVESTMENT , 2001 .

[16]  Bennett Fox,et al.  SEMI-MARKOV PROCESSES: A PRIMER , 1968 .

[17]  Yi Jiang,et al.  SIMULATION APPROACH TO PREDICTION OF HIGHWAY STRUCTURE CONDITIONS , 1992 .

[18]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[19]  John O Sobanjo,et al.  Comparison of Markov Chain and Semi-Markov Models for Crack Deterioration on Flexible Pavements , 2013 .

[20]  Richard Shepard,et al.  Bridge Management for the 21st Century , 2000 .

[21]  Pablo Luis Durango-Cohen,et al.  Maintenance and repair decision making for infrastructure facilities without a deterioration model , 2004 .