Classifying structural condition of deteriorating stormwater pipes using support vector machine

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Tran, Huu Dung and Ng, A. W. M (2010) Classifying structural condition of deteriorating stormwater pipes using support vector machine. In: Pipelines 2010 : Climbing New Peaks to Infrastructure Reliability--Renew, Rehab, and Reinvest. ASCE, Reston, Virginia US, pp. 857-866.


Stormwater pipe networks are an important part of the water infrastructure that removes stormwater runoff from urban cities of Australia. This paper presents the attempts made to improve the classification or prediction of structural condition for individual stormwater pipes. Such predictive information can be used to prioritize pipes for inspection and subsequent maintenance actions and thus support water utilities implementing their proactive management strategies. In this paper, a support vector machine (SVM) was developed, tested and compared against the back‐propagation neural network (BPNN) model which has been used in a previous study. The developed model was applied to a case study using a sample of CCTV inspected pipes and corresponding pipe factors. The results showed that with regards to correctly predicting structural condition of individual stormwater pipes, the SVM model outperformed the BPNN significantly in the train dataset and marginally in the test dataset. Several advantages of the SVM model were also found compared to the BPNN model.

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ASCE conference proceedings

Item type Book Section
Official URL
ISBN 9780784411384
Subjects Historical > FOR Classification > 0905 Civil Engineering
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID19508, deterioration, pipes, stormwater management, classification, support vector machine, neural network, prediction
Citations in Scopus 3 - View on Scopus
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