Hybrid machine learning-based model for predicting chloride ion concentration in coral aggregate concrete and its ethically aligned graphical user interface design

The chloride ion concentration distribution in coral aggregate concrete (CAC-CC) holds significant importance for evaluating CAC performance, assessing rebar corrosion, and understanding chloride ion diffusion. Thus, this study employs hybrid machine learning algorithms to establish predictive models for CAC-CC under three environmental conditions (salt spray zone, tidal zone, and underwater zone) and evaluates the accuracy and generalization ability of these models. The results demonstrate that the genetic algorithm-optimized support vector regression (GA-SVR) model provides predictions closer to the actual values, exhibiting smoother error fluctuations. The GA-SVR model also exhibits smaller mean and standard deviation in the residual distribution. Furthermore, the GA-SVR model excels in five performance evaluation metrics, with R2, MAE, MAPE, MSE, and RMSE values of 0.986, 1.25e-2, 3.24e-2, 0.276e-3, and 1.66e-2, respectively. Hence, the GA-SVR model emerges as the optimal choice for CAC-CC prediction. Additionally, this work utilizes Shapley Additive Explanations (SHAP) to assess the contribution of eight features. The analysis reveals that the water-binder ratio and the pre-wetted water to total water ratio are the two most critical features. Moreover, an increase in the pre-wetted water to total water ratio and a decrease in the water-cement ratio hinder chloride ion diffusion. Mechanistic analysis of the feature contribution to CAC-CC is further explored in this study. Based on the GA-SVR model, a graphical user interface for CAC-CC has been developed, enabling visualization of CAC-CC predictions. This research introduces a novel approach to optimize CAC performance and predict chloride ion concentration, laying the foundation for CAC application in reef construction.