Prediction of Sewer Condition Grade Using Support Vector Machines

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Mashford, John, Marlow, David, Tran, Huu Dung and May, Robert (2011) Prediction of Sewer Condition Grade Using Support Vector Machines. Journal of Computing in Civil Engineering, 25 (4). pp. 283-290. ISSN 0887-3801 (print) 1943-5487 (online)

Abstract

Assessing the condition of sewer networks is an important asset management approach. However, because of high inspection costs and limited budget, only a small proportion of sewer systems may be inspected. Tools are therefore required to help target inspection efforts and to extract maximum value from the condition data collected. Owing to the difficulty in modeling the complexities of sewer condition deterioration, there has been interest in the application of artificial intelligence-based techniques such as artificial neural networks to develop models that can infer an unknown structural condition based on data from sewers that have been inspected. To this end, this study investigates the use of support vector machine (SVM) models to predict the condition of sewers. The results of model testing showed that the SVM achieves good predictive performance. With access to a representative set of training data, the SVM modeling approach can therefore be used to allocate a condition grade to sewer assets with reasonable confidence and thus identify high risk sewer assets for subsequent inspection.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/8892
DOI https://doi.org/10.1061/(ASCE)CP.1943-5487.0000089
Official URL http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29C...
Subjects Historical > Faculty/School/Research Centre/Department > School of Sport and Exercise Science
Current > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9004 Water and Waste Services
Keywords ResPubID22953, sewers, artificial intelligence, predictions, inspection, costs
Citations in Scopus 35 - View on Scopus
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