Prediction of chloride ion concentration distribution in basalt-polypropylene fiber reinforced concrete based on optimized machine learning algorithm

Abstract

The distribution characteristics of chloride ion concentration in basalt-polypropylene fiber reinforced concrete (BPFRC-CC) are important for assessing the durability of concrete structures under marine environment. This study collected the distribution data of BPFRC-CC in three different environments: tidal, submersible, and salt spray. Several optimized machine learning models were developed to predict the distribution of BPFRC-CC. The performance of these models was evaluated based on a comparison between actual and predicted values, residual distribution histograms, and performance indicators. The research results of this article showed that the particle swarm optimization support vector regression (PSO-SVR) model exhibits significant accuracy and generalization ability, with closer proximity to the actual values in both the training and testing sets. The residual distribution histogram of the PSO-SVR model approximated a normal distribution with a smaller mean and standard deviation. Evaluation indicators, including R2, MSE, RMSE, MAE, and MAPE, for the PSO-SVR model were 0.994, 6.92e-5, 0.0083, 0.0062, and 0.0453, respectively. The SHAP values revealed that depth and age were the most critical influencing factors. The decrease in age, water-cement ratio, and water-binder ratio, the addition of basalt fibers and polypropylene fibers, and the increase in depth reduced the BPFRC-CC. In addition, BPFRC-CC in tidal, salt spray, and submersible areas decreased in that order. Finally, a graphical user interface for BPFRC-CC was developed, providing researchers and production personnel with an intuitive, efficient, and visual production experience. This study has important implications for the optimization of BPFRC formulations and the prediction and assessment of the life of BPFRC structures.