Predicting Microbiologically Influenced Concrete Corrosion in Self-cleansing Sewers using Meta-learning Techniques

Abstract

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 (MLR) model to predict the MIC in sewer pipelines. The models can be categorized into three sections: (i) stand-alone models (GMDH, GRNN, RBFNN, MLPNN, CHAID, and CART), (ii) integrative models (ANFIS and SVR with PSO, ABC and FA) and (iii) ensemble meta-learner Stepwise Regression model. After implementing the models, statistical measures, including RMSE, MAE, MBE, PCC, and Nash–Sutcliffe model efficiency (NSE) 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 percent. The durability and lifespan of the sewer system is also estimated by 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 years less) exposed to submerged sewage than the gas phase with 56 years of durability.