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Urban Water Journal Volume 3, 2006 - Issue 3
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Original Articles

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

D. H. Tran School of Architectural, Civil and Mechanical Engineering, Victoria University, PO Box 14428, Melbourne, 8001, Victoria, AustraliaCorrespondence[email protected]
,
A. W. M. Ng School of Architectural, Civil and Mechanical Engineering, Victoria University, PO Box 14428, Melbourne, 8001, Victoria, Australia
,
B. J. C. Perera School of Architectural, Civil and Mechanical Engineering, Victoria University, PO Box 14428, Melbourne, 8001, Victoria, Australia
,
S. Burn CSIRO Manufacturing & Infrastructure Technology, PO Box 56, Highett, Victoria, 3190, Australia
&
P. Davis CSIRO Manufacturing & Infrastructure Technology, PO Box 56, Highett, Victoria, 3190, Australia
Pages 175-184 | Published online: 16 Feb 2007

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Srinath Shiv Kumar, Dulcy Abraham & Juyeong Choi. (2022) A hybrid framework for mining spatial characteristics of sewer defects from inspection databases. Urban Water Journal 19:1, pages 51-61.
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Vitor Sousa, José P. Matos, Natércia Matias & Inês Meireles. (2019) Statistical comparison of the performance of data-based models for sewer condition modeling. Structure and Infrastructure Engineering 15:12, pages 1680-1693.
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Nicolas Caradot, Hauke Sonnenberg, Ingo Kropp, Alexander Ringe, Stephane Denhez, Andreas Hartmann & Pascale Rouault. (2017) The relevance of sewer deterioration modelling to support asset management strategies. Urban Water Journal 14:10, pages 1007-1015.
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Ming-Der Yang, Yi-Ping Chen, Tung-Ching Su & Yu-Hao Lin. (2017) Sewer pipe defects diagnosis assessment using multivariate analysis on CCTV video imagery. Urban Water Journal 14:5, pages 475-482.
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E.V. Ana & W. Bauwens. (2010) Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods. Urban Water Journal 7:1, pages 47-59.
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Articles from other publishers (44)

Sherif Abdelkhalek & Tarek Zayed. (2023) A multi-tier deterioration assessment models for sewer and stormwater pipelines in Hong Kong. Journal of Environmental Management 345, pages 118913.
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Comfort Salihu, Saeed Reza Mohandes, Ahmed Farouk Kineber, M. Reza Hosseini, Faris Elghaish & Tarek Zayed. (2023) A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence. Buildings 13:4, pages 952.
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Xuming Zeng, Zinan Wang, Hao Wang, Shengyan Zhu & Shaofeng Chen. (2023) Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models. Sustainability 15:4, pages 3849.
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Guoyang FuJayantha Kodikara. (2022) Physical Model–Based Failure Prediction of Concrete Stormwater Pipes Subjected to Rebar Corrosion. Journal of Pipeline Systems Engineering and Practice 13:4.
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Saeed Reza Mohandes, Ahmed Farouk Kineber, Sherif Abdelkhalek, Khalid Kaddoura, Moustafa Elsayed, M. Reza Hosseini & Tarek Zayed. (2022) Evaluation of the critical factors causing sewer overflows through modeling of structural equations and system dynamics. Journal of Cleaner Production 375, pages 134035.
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Lewis O. Makana, Will J. Shepherd, Simon Tait, Christopher D. F. Rogers, Nicole Metje, Joby B. Boxall & Alma N. A. Schellart. (2022) Future Inspection and Deterioration Prediction Capabilities for Buried Distributed Water Infrastructure. Journal of Pipeline Systems Engineering and Practice 13:3.
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Comfort Salihu, Mohamed Hussein, Saeed Reza Mohandes & Tarek Zayed. (2022) Towards a comprehensive review of the deterioration factors and modeling for sewer pipelines: A hybrid of bibliometric, scientometric, and meta-analysis approach. Journal of Cleaner Production 351, pages 131460.
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Jingyi Qi, Michael Smith & Nicole Barclay. (2022) Empirical Data-Based Condition Prediction for Stormwater Pipelines with Machine Learning. Empirical Data-Based Condition Prediction for Stormwater Pipelines with Machine Learning.
Ma’mon M. Hatmal, Walhan Alshaer, Ismail S. Mahmoud, Mohammad A. I. Al-Hatamleh, Hamzeh J. Al-Ameer, Omar Abuyaman, Malek Zihlif, Rohimah Mohamud, Mais Darras, Mohammad Al Shhab, Rand Abu-Raideh, Hilweh Ismail, Ali Al-Hamadi & Ali Abdelhay. (2021) Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis. PLOS ONE 16:10, pages e0257857.
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Yiqi Wu, Simon TaitAndrew Nichols & Jamil Raja. (2021) Simulation of Railway Drainage Asset Service Condition Degradation in the UK Using a Markov Chain–Based Approach. Journal of Infrastructure Systems 27:3.
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Mohammadreza Malek MohammadiMohammad NajafiSharareh KermanshachiVinayak Kaushal & Ramtin Serajiantehrani. (2020) Factors Influencing the Condition of Sewer Pipes: State-of-the-Art Review. Journal of Pipeline Systems Engineering and Practice 11:4.
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Jeroen Verheugd, Paulo R de Oliveira da Costa, Reza Refaei Afshar, Yingqian Zhang & Sjoerd Boersma. (2020) Predicting Water Pipe Failures with a Recurrent Neural Hawkes Process Model. Predicting Water Pipe Failures with a Recurrent Neural Hawkes Process Model.
Xianfei Yin, Yuan Chen, Ahmed Bouferguene & Mohamed Al-Hussein. (2020) Data-driven bi-level sewer pipe deterioration model: Design and analysis. Automation in Construction 116, pages 103181.
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Tuija Laakso, Teemu Kokkonen, Ilkka Mellin & Riku Vahala. (2018) Sewer Condition Prediction and Analysis of Explanatory Factors. Water 10:9, pages 1239.
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N. Caradot, M. Riechel, M. Fesneau, N. Hernandez, A. Torres, H. Sonnenberg, E. Eckert, N. Lengemann, J. Waschnewski & P. Rouault. (2018) Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany. Journal of Hydroinformatics 20:5, pages 1131-1147.
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Szeląg Bartosz, Adam Kiczko, Jan Studziński & Lidia Dąbek. (2018) Hydrodynamic and probabilistic modelling of storm overflow discharges. Journal of Hydroinformatics 20:5, pages 1100-1110.
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Hind El-HousniMaxim OuelletSophie Duchesne. (2018) Identification of most significant factors for modeling deterioration of sewer pipes. Canadian Journal of Civil Engineering 45:3, pages 215-226.
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A. Kalinichenko, L. Arseniyeva & V. Pasichnyi. (2017) ELECTRONIC NOSE AND PROBABILISTIC NEURAL NETWORK USE FOR SAUSAGES IDENTIFICATION. Bulletin of Taras Shevchenko National University of Kyiv. Chemistry:2(54), pages 47-51.
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Marius Møller Rokstad & Rita Maria Ugarelli. (2015) Evaluating the role of deterioration models for condition assessment of sewers. Journal of Hydroinformatics 17:5, pages 789-804.
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Indranil Seth. (2015) Use of Artificial Neural Networks and Genetic Algorithms in Urban Water Management: A Brief Overview. Journal - American Water Works Association 107:5, pages 93-97.
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Sophie DuchesneKassandra BouchardBabacar ToumbouJean-Pierre Villeneuve. (2014) Assessing the impact of renewal scenarios on the global structural state of sewer pipe networks. Canadian Journal of Civil Engineering 41:8, pages 761-768.
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Vitor Sousa, José P. Matos & Natércia Matias. (2014) Evaluation of artificial intelligence tool performance and uncertainty for predicting sewer structural condition. Automation in Construction 44, pages 84-91.
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Recep Gorguluarslan, Eui-Soo Kim, Seung-Kyum Choi & Hae-Jin Choi. (2014) Reliability estimation of washing machine spider assembly via classification. The International Journal of Advanced Manufacturing Technology 72:9-12, pages 1581-1591.
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P. Davis, E. Sullivan, D. Marlow & D. Marney. (2013) A selection framework for infrastructure condition monitoring technologies in water and wastewater networks. Expert Systems with Applications 40:6, pages 1947-1958.
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Sophie Duchesne, Guillaume Beardsell, Jean‐Pierre Villeneuve, Babacar Toumbou & Kassandra Bouchard. (2012) A Survival Analysis Model for Sewer Pipe Structural Deterioration. Computer-Aided Civil and Infrastructure Engineering 28:2, pages 146-160.
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John MashfordDavid MarlowDung TranRobert May. (2011) Prediction of Sewer Condition Grade Using Support Vector Machines. Journal of Computing in Civil Engineering 25:4, pages 283-290.
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D. H. TranB. J. C. PereraA. W. M. Ng. (2010) Hydraulic Deterioration Models for Storm-Water Drainage Pipes: Ordered Probit versus Probabilistic Neural Network. Journal of Computing in Civil Engineering 24:2, pages 140-150.
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H. D. TranB. J. C. PereraA. W. M. Ng. (2009) Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models. Journal of Water Resources Planning and Management 135:6, pages 553-557.
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