Research Repository

Hydraulic Deterioration Models for Storm-Water Drainage Pipes: Ordered Probit versus Probabilistic Neural Network

Tran, Huu Dung, Perera, B. J. C and Ng, A. W. M (2010) Hydraulic Deterioration Models for Storm-Water Drainage Pipes: Ordered Probit versus Probabilistic Neural Network. Journal of Computing in Civil Engineering, 24 (2). pp. 140-150. ISSN 0887-3801

Full text for this resource is not available from the Research Repository.


A decrease in discharge capacity of storm-water drainage pipes is the result of the so-called hydraulic deterioration which reduces the cross sectional area of pipes and increases the pipe roughness. Hydraulic deterioration is caused by tree root intrusion, sediment accumulation, and encrustation, and is affected by many influential factors such as pipe size and pipe location. Predicting hydraulic deterioration is important for effective management of drainage pipes. An ordered probit deterioration model (OPDM) and a probabilistic neural network deterioration model (PNNDM) were developed in this study using the influential factors as model inputs and the hydraulic condition as model output. Their predictive performances were compared against each other using a case study from Melbourne, Australia with a sample of 417 storm-water drainage pipes subjected to closed circuit television inspection. The results show that the PNNDM is more suitable for predicting the hydraulic deterioration and outperforms the OPDM. Several input factors such as pipe size and pipe age are found significant to the hydraulic deterioration.

Item Type: Article
Uncontrolled Keywords: ResPubID19506, stormwater management, management of drainage pipes, storm-water drainage pipe discharge capacity, predicting hydraulic deterioration
Subjects: Current > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9004 Water and Waste Services
Historical > Faculty/School/Research Centre/Department > Institute for Sustainability and Innovation (ISI)
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 03 Nov 2011 02:47
Last Modified: 20 May 2014 06:18
ePrint Statistics: View download statistics for this item
Citations in Scopus: 15 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar