Title

Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion

RIS ID

132843

Publication Details

Li, X., Khademi, F., Liu, Y., Akbari, M., Wang, C., Bond, P. L., Keller, J. & Jiang, G. (2019). Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion. Journal of Environmental Management, 234 431-439.

Abstract

Concrete corrosion is one of the most significant failure mechanisms of sewer pipes, and can reduce the sewer service life significantly. To facilitate the management and maintenance of sewers, it is essential to obtain reliable prediction of the expected service life of sewers, especially if that is based on limited environmental conditions. Recently, a long-term study was performed to identify the controlling factors of concrete sewer corrosion using well-controlled laboratory-scale corrosion chambers to vary levels of H2S concentration, relative humidity, temperature and in-sewer location. Using the results of the long-term study, three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS), as well as the interaction between environmental parameters, were assessed for predicting the corrosion initiation time (ti) and corrosion rate (r). This was performed using the sewer environmental factors as the input under 12 different scenarios after allowing for an initiation corrosion period. ANN and ANFIS models showed better performance than MLR models, with or without considering the interactions between environmental factors. With the limited input data available, it was observed that ti prediction by these models is quite sensitive, however, they are more robust for predicting r as long as the H2S concentration is available. Using the H2S concentration as a single input, all three data driven models can reasonably predict the sewer service life.

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.jenvman.2018.12.098