On the implementation of the interpretable data-intelligence model for designing service life of structural concrete in a marine environment

Abstract

Modeling the durability design of reinforced concrete structures is a particularly crucial challenge for identifying the behavior of marine concrete. In this research, novel interpretable equation-based models based on Fick's second law of diffusion for designing the service life of structural concrete were presented. To do so, a Machine Learning (ML) approach based on linear and non-linear methods including Multivariate Adaptive Regression Splines (MARS), Gene Expression Programming (GEP), and M5p Model Tree (MT) was performed to predict the apparent surface chloride concentration (Cs) in the zones of the marine environment. A comprehensive database is comprising of 642 field exposure experimental and environmental records was utilized to develop ML methods. The proposed ML models were investigated in terms of performance evaluation, error measurements, and visual consideration, MARS model (r=0.890,WI=93.9%andRMSE=0.804%bindercontent, (tidal zone) and r=0.901,WI=93.5%andRMSE=0.818%bindercontent (splash zone) and r=0.875,WI=86.8%andRMSE=0.883%bindercontent (submerged zone)) outperformed the proposed GEP, MT and empirical equations. Furthermore, simulation of Monte-Carlo analysis was extended to verify the proposed ML predictive models. Evaluation of the sensitivity analysis presented that the water to binder ratio, annual mean temperature, and exposure time were the most influential factors in predicting the Cs of reinforced concrete structures. Also, a parametric assessment has been performed for the presentation of the robustness of the proposed ML model. The modeling results presented new insight into the designing of the service life of reinforced concrete with superiority promotion of ML methods.