Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems

Accurate prediction of the current and future conditions of wastewater systems using available assessment data is crucial for developing appropriate proactive maintenance and rehabilitation strategies for an aging wastewater collection and conveyance system. This paper proposes a method to estimate the transition probabilities of different condition states in Markov chain-based deterioration models for wastewater systems using an ordered probit model. The proposed model is applied and evaluated using the condition data of sewer pipes managed by the City of San Diego's Metropolitan Wastewater Department. The developed model presents some advantages in estimating transition probabilities over the approaches developed in the past, including the nonlinear optimization-based approach, in terms of versatility in the implementation, precision of the estimated data, and appropriateness of the assumptions in the model. The paper concludes that the ordered probit model approach is a statistically sound and robust method; however, in order to gain greater accuracy in deterioration modeling, periodic assessment of the wastewater systems with more data types is desirable.

[1]  Moshe Ben-Akiva,et al.  ESTIMATION OF HIGHWAY PAVEMENT DETERIORATION FROM IN-SERVICE PAVEMENT DATA , 1990 .

[2]  Moshe E. Ben-Akiva,et al.  An Approach for Predicting Latent Infrastructure Facility Deterioration , 1993, Transp. Sci..

[3]  Richard N. Palmer,et al.  Expert System for Prioritizing the Inspection of Sewers: Knowledge Base Formulation and Evaluation , 2002 .

[4]  R. A Fenner,et al.  Approaches to sewer maintenance: a review , 2000 .

[5]  Samer Madanat,et al.  Estimation of infrastructure transition probabilities from condition rating data , 1995 .

[6]  Yi Jiang,et al.  BRIDGE PERFORMANCE PREDICTION MODEL USING THE MARKOV CHAIN , 1988 .

[7]  Sheldon M. Ross Introduction to Probability Models. , 1995 .

[8]  Matthew G. Karlaftis,et al.  Probabilistic Infrastructure Deterioration Models with Panel Data , 1997 .

[9]  Samuel T. Ariaratnam,et al.  Assessment of Infrastructure Inspection Needs Using Logistic Models , 2001 .

[10]  Samer Madanat,et al.  Poisson Regression Models of Infrastructure Transition Probabilities , 1995 .

[11]  Eric R. Zieyel Operations research : applications and algorithms , 1988 .

[12]  Dulcy M. Abraham,et al.  Assessment technologies for sewer system rehabilitation , 1998 .

[13]  Moshe Ben-Akiva,et al.  LATENT PERFORMANCE APPROACH TO INFRASTRUCTURE MANAGEMENT , 1991 .

[14]  J. M. Makar Diagnostic Techniques for Sewer Systems , 1999 .

[15]  Y-J Lee,et al.  ECONOMETRIC MODEL FOR PREDICTING DETERIORATION OF BRIDGE DECK EXPANSION JOINTS , 2003 .

[16]  Samer Madanat,et al.  Semiparametric Hazard Rate Models of Reinforced Concrete Bridge Deck Deterioration , 2001 .

[17]  Kumares C. Sinha,et al.  Comparison of Methodologies to Predict Bridge Deterioration , 1997 .

[18]  Moshe Ben-Akiva,et al.  Modeling Infrastructure Performance and User Costs , 1995 .

[19]  G. Kuczera,et al.  Markov Model for Storm Water Pipe Deterioration , 2002 .

[20]  Dulcy M. Abraham,et al.  CHALLENGING ISSUES IN MODELING DETERIORATION OF COMBINED SEWERS , 2001 .

[21]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[22]  George Morcous,et al.  Modeling Bridge Deterioration Using Case-Based Reasoning , 2002 .

[23]  Samuel H Carpenter,et al.  PAVEMENT PERFORMANCE PREDICTION MODEL USING THE MARKOV PROCESS , 1987 .

[24]  Samer Madanat,et al.  Using Duration Models to Analyze Experimental Pavement Failure Data , 2000 .

[25]  Wayne J. Davis,et al.  Optimal maintenance decisions for pavement management , 1987 .

[26]  Yi Jiang,et al.  BRIDGE SERVICE LIFE PREDICTION MODEL USING THE MARKOV CHAIN , 1989 .

[27]  Yehuda Kleiner,et al.  Scheduling Inspection and Renewal of Large Infrastructure Assets , 2001 .

[28]  Samer Madanat,et al.  Computation of Infrastructure Transition Probabilities using Stochastic Duration Models , 2002 .