A Survival Analysis Model for Sewer Pipe Structural Deterioration

The structural state of sewer systems are often quantified using condition classes. The classes are based on the severity of structural defects observed on individual pipes within the system. This paper developed a survival analysis model to predict the overall structural state of a sewer network based on camera inspection data from a sample of pipes in the system. The convolution product was used to define the survival functions for cumulative staying times in each condition class. An original calibration procedure for the sewer deterioration model was developed to overcome the censored nature of data available for the calibration of sewer deterioration models. The exponential and Weibull functions were used to represent the distribution of waiting times in each deterioration state. Cross-validation tests showed that the Weibull function led to greater uncertainty than the exponential function for the simulated proportion of pipes that are in a deteriorated state. The cross-validation tests also showed that the model's results are robust to smaller calibration sample sizes using various sample sizes for model calibration. The model's potential for predicting the overall state of deterioration of a sewer network when only a small proportion of the pipes have been inspected is confirmed.

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

[2]  Dimitri A. Grivas,et al.  Method for Estimating Transition Probability in Bridge Deterioration Models , 1998 .

[3]  W. Bauwens,et al.  Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods , 2010 .

[4]  Sophie Duchesne,et al.  Modélisation de l'évolution de l'état structural des réseaux d'égout : application à une municipalité du Québec , 2000 .

[5]  Rehan Sadiq,et al.  Modelling the deterioration of buried infrastructure as a fuzzy Markov process , 2006 .

[6]  Y. Le Gat,et al.  Modelling the deterioration process of drainage pipelines , 2008 .

[7]  Anne Ng,et al.  Comparison of Structural Deterioration Models for Stormwater Drainage Pipes , 2009, Comput. Aided Civ. Infrastructure Eng..

[8]  R Baur,et al.  Selective inspection planning with ageing forecast for sewer types. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[9]  Samuel T. Ariaratnam,et al.  Prediction models for sewer infrastructure utilizing rule-based simulation , 2004 .

[10]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

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

[12]  Dulcy M. Abraham,et al.  Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems , 2006 .

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

[14]  Anne Ng,et al.  Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models , 2009 .

[15]  Paul Davis,et al.  Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes , 2006 .

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

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

[18]  F H L R Clemens,et al.  Probabilistic modeling of sewer deterioration using inspection data. , 2008, Water science and technology : a journal of the International Association on Water Pollution Research.

[19]  Denys Breysse,et al.  Management Strategies and Improvement of Performance of Sewer Networks , 2007, Comput. Aided Civ. Infrastructure Eng..

[20]  Franck Schoefs,et al.  Comparison of Additional Costs for Several Replacement Strategies of Randomly Ageing Reinforced Concrete Pipes , 2009, Comput. Aided Civ. Infrastructure Eng..

[21]  Wided Ben Tagherouit,et al.  A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks , 2011, Comput. Aided Civ. Infrastructure Eng..

[22]  Anne Ng,et al.  Hydraulic Deterioration Models for Storm-Water Drainage Pipes: Ordered Probit versus Probabilistic Neural Network , 2010 .

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

[24]  Richard Fenner,et al.  A new approach for directing proactive sewer maintenance , 2000 .

[25]  Nanxiang Li,et al.  Development of a new asphalt pavement performance prediction model , 1997 .