Decision Tree–Based Deterioration Model for Buried Wastewater Pipelines

AbstractAsset management provides a managerial decision-making framework for public agencies to monitor, evaluate, and make informed decisions about how to best maintain vital civil infrastructure assets. Among many steps required for implementing asset management, developing an accurate deterioration model is one of the key components because it helps infrastructure agencies predict remaining asset life. The accuracy of deterioration models highly depends on the quality of input data and the computational technique used in data analysis. Among many options of computational techniques, a decision tree offers the combination of visual representation and sound statistical background. The visual representation enables the decision maker to identify the relationship and interdependencies of each decision and formulate an appropriate prediction. This study developed a decision tree–based deterioration model for sewer pipes. The performance of the new model is then compared with conventional regression- and neu...

[1]  Dimitri P. Solomatine,et al.  M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China , 2004 .

[2]  Nong Ye,et al.  The Handbook of Data Mining , 2003 .

[3]  Balvant Rajani,et al.  Comprehensive review of structural deterioration of water mains: statistical models , 2001 .

[4]  Jay L. Devore,et al.  Probability and statistics for engineering and the sciences , 1982 .

[5]  A. Samer Ezeldin,et al.  Neural Networks for Estimating the Productivity of Concreting Activities , 2006 .

[6]  Simaan M. AbouRizk,et al.  Assessing Residual Value of Heavy Construction Equipment Using Predictive Data Mining Model , 2008 .

[7]  Patricia B. Cerrito Introduction to Data Mining Using SAS Enterprise Miner , 2006 .

[8]  Michael H. Kutner Applied Linear Statistical Models , 1974 .

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[11]  Ali A. Al-Subaihi Variable Selection in Multivariable Regression Using SAS/IML , 2002 .

[12]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[13]  Kerry J. McManus,et al.  Prediction of Water Pipe Asset Life Using Neural Networks , 2007 .

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[15]  Reini D Wirahadikusumah,et al.  Optimization modeling for management of large combined sewer networks , 1999 .

[16]  Tarek Zayed,et al.  Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines , 2008 .

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

[18]  Ian F. C. Smith,et al.  Improving System Identification Using Clustering , 2008 .

[19]  David E. Booth,et al.  Multivariate statistical inference and applications , 1997 .

[20]  Thair Nu Phyu Survey of Classification Techniques in Data Mining , 2009 .

[21]  B. J. C. Perera,et al.  Neural networks deterioration models for serviceability condition of buried stormwater pipes , 2007, Eng. Appl. Artif. Intell..