Predicting Microbiologically Influenced Concrete Corrosion in Self-Cleansing Sewers Using Meta-Learning Techniques

Sewer networks are not only necessary as an infrastructure for human societies, but they can also help humans achieve a stable situation with the surrounding natural environment by controlling and preventing the spread of pollution in the environment. As a result, concrete sewer maintenance and analysis of their damaging elements are critical. In this regard, modeling microbiologically influenced corrosion (MIC) is a challenging phenomenon. Due to the complicated aspects related to the interaction of microorganisms and concrete degradation, this research suggests several machine-learning models as well as traditional multiple linear regression model to predict the MIC in sewer pipelines. The models can be categorized into three sections: (i) stand-alone models (group method of data handling, generalized regression neural network, radial basis function neural network, multilayer perceptron neural network, chi-square automatic interaction detection, and classification and regression tree); (ii) integrative models (adaptive neuro-fuzzy inference system and support vector regression with particle swarm optimization, artificial bee colony, and firefly algorithm); and (iii) ensemble meta-learner stepwise regression (SR) model. After implementing the models, statistical measures, including root mean square error, mean absolute error, mean bias error, Pearson correlation coefficient, and Nash-Sutcliffe model efficiency are considered for evaluating models’ performances. The results indicate that the ensemble meta-learner-SR model is significantly more precise than other models. They also demonstrate that using an integrative model can improve the accuracy of stand-alone models by at least up to 42%. The durability and lifespan of the sewer system are also estimated with the aid of the best predictive model (meta-learner-SR) for two scenario cases of (i) gas phase and (ii) submerged conditions. It is concluded that the sewer systems have a considerably lower life span (24 y less) exposed to submerged sewage than the gas phase with 56 y of durability.