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Predicting concrete corrosion of sewers using artificial neural network

Journal Article


Abstract


  • Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

UOW Authors


  •   Jiang, Guangming
  •   Keller, Jurg (external author)
  •   Bond, Philip (external author)
  •   Yuan, Zhiguo (external author)

Publication Date


  • 2016

Citation


  • Jiang, G., Keller, J., Bond, P. L. & Yuan, Z. (2016). Predicting concrete corrosion of sewers using artificial neural network. Water Research, 92 52-60.

Scopus Eid


  • 2-s2.0-84956720692

Number Of Pages


  • 8

Start Page


  • 52

End Page


  • 60

Volume


  • 92

Place Of Publication


  • United Kingdom

Abstract


  • Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

UOW Authors


  •   Jiang, Guangming
  •   Keller, Jurg (external author)
  •   Bond, Philip (external author)
  •   Yuan, Zhiguo (external author)

Publication Date


  • 2016

Citation


  • Jiang, G., Keller, J., Bond, P. L. & Yuan, Z. (2016). Predicting concrete corrosion of sewers using artificial neural network. Water Research, 92 52-60.

Scopus Eid


  • 2-s2.0-84956720692

Number Of Pages


  • 8

Start Page


  • 52

End Page


  • 60

Volume


  • 92

Place Of Publication


  • United Kingdom