Exploring the Relationship between Soil Properties and Deterioration of Metallic Pipes Using Predictive Data Mining Methods

Abstract

/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1061/(ASCE) CP.1943-5487.0000032 Journal of Computing in Civil Engineering, 24, 3, pp. 289-301, 2010-05-01 Exploring the relationship between soil properties and deterioration of metallic pipes using predictive data mining methods Liu, Z.; Sadiq, R.; Rajani, B. B.; Najjaran, H.http://www.nrc-cnrc.gc.ca/irc Ex ploring t he re la t ionship be t w e e n soil prope rt ie s a nd de t e riora t ion of m e t a llic pipe s using pre dic t ive da t a m ining m e t hods NRCC-53534Liu, Z.; Sadiq, R.; Rajani, B.B.; Najjaran, H. AbstractSoil corrosivity is considered to be a major factor for the deterioration of metallic water mains. Using a 10-point scoring method as suggested by the American Water Works Association, soil corrosivity potential can be estimated by five soil properties: (1) resistivity, (2) pH value, (3) redox potential, (4) sulfide, and (5) percentage of clay fines. However, the relationship between soil corrosivity and pipe deterioration is often ambiguous and not well defined.In order to identify the direct relationship between soil properties and pipe deterioration, which is defined as the ratio of the maximum pit depth to pipe age, predictive data mining approaches are investigated in this study. Both single-and multi-predictor based approaches are employed to model such relationship. The advantage of combining multiple predictors is also demonstrated. Among all approaches, rotation forest achieves the best result in terms of the prediction error to estimate pipe deterioration rate. Compared to the random forest method, which is the next to the best, the normalized mean square error decreased 50%.With the proposed approaches, the assessment of pipe condition can be achieved by analyz-

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