Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

Abstract

The addition of rubber to concrete improves resistance to chloride ion attacks. Therefore, rapidly determining the chloride permeability coefficient (DCI) of rubber concrete (RC) can contribute to promotion in coastal areas. Most current methods for determining DCI of RC are traditional, which cannot account for multi-factorial effects and suffer from low prediction accuracy. Machine learning (ML) techniques have good non-linear learning capabilities and can consider the effects of multiple factors compared with traditional methods. However, ML models easily fall into the local optimum due to their parameters’ influence. Therefore, a mixed whale optimization algorithm (MWOA) was developed in this paper to optimize ML models. The main strategies are to introduce Tent mapping to expand the search range of the algorithm, to use an adaptive t-distribution dimension-by-dimensional variation strategy to perturb the optimal fitness individual to thereby improve the algorithm’s ability to jump out of the local optimum, and to introduce adaptive weights and adaptive probability threshold values to enhance the adaptive capacity of the algorithm. For this purpose, data were collected from the published literature. Three machine learning models, Extreme Learning Machine (ELM), Random Forest (RF), and Elman Neural Network (ELMAN), were built to predict the DCI of RC, and the three models were optimized using MWOA. The calculations show that the MWOA is effective with the optimized ELM, RF, and ELMAN models improving the prediction accuracy by 54.4%, 62.9%, and 36.4% compared with the initial model. The MWOA-ELM model was found to be the optimal model after a comparative analysis. The accuracy of the multiple linear regression model (MRL) and the traditional mathematical model is calculated to be 87.15% and 85.03%, which is lower than that of the MWOA-ELM model. This indicates that the ML model that is optimized using the improved whale optimization algorithm has better predictive ability than traditional models, providing a new option for predicting the DCI of RC.

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

“…With the rise of artificial intelligence, machine learning shows great potential in the field of concrete science. It can not only well describe the complex properties of concrete and the nonlinear relationship between various properties, but also accurately describe the relationship between independent variables with higher fitting effect and accuracy [114,115].…”

Assessment of chlorine resistance in concrete in the tidal range and splash zone of a torrid marine region

“…Amin et al (Amin et al, 2022) used a genetic programming approach to investigate rapid chloride ion penetration (RCP), while Imounga et al (Imounga et al, 2020) assessed chloride ingress into reinforced concrete by Bayesian networks, and Mukhti et al (Mukhti et al, 2023) evaluated early concrete damage caused by chloride-induced steel corrosion using a deep learning approach based on RNN for ultrasonic pulse waves. Huang et al (Huang et al, 2023) predicted the chloride permeability coefficient based on improved machinelearning techniques, and Li et al (Li et al, 2021) used the posable set theory to analyze and calculate the weights of factors acting on durability, which were combined with a fuzzy evaluation method. Chen et al (Chen et al, 2015) evaluated the durability of in-service concrete using an improved three-scale hierarchical analysis method and fuzzy topology theory, while Cai et al (Cai et al, 2019) evaluated the durability of concrete in a chloride ion attack environment using a fuzzy integrated evaluation method.…”