Designing fiber-reinforced concrete (FRC) panels to resist projectile impact is a challenging task, as it requires balancing the panel's failure resistance with construction feasibility, such as minimizing its weight. These objectives often conflict since lighter FRC panels tend to be more vulnerable to damage than heavier ones. Additionally, the panels must be designed to achieve predefined failure modes. To address these challenges, this study develops a multi-objective optimization process to minimize both the penetration ratio and the weight of FRC panels under missile impact while incorporating failure mode as a constraint. The optimization process is implemented using the nondominated sorting genetic algorithm-II (NSGA-II). Machine learning (ML) models are employed to predict penetration depth and classify failure modes using experimental datasets. However, due to the dataset's limitations, including class imbalance and insufficient samples, the k-means SMOTE technique is applied to generate additional data for the minor classes. Moreover, the Giant Trevally Optimizer (GTO) is utilized to adjust the hyperparameters of the ML models, aiming to achieve optimal performance. The results demonstrate that kmeans-SMOTE and GTO algorithms significantly improve the predictive accuracy of the models. Furthermore, the optimization algorithm effectively identifies multiple optimal solutions, exhibiting a clear trade-off between the objectives. The strong convergence toward boundary values and the even distribution of Pareto-front points confirms the algorithm's efficiency in exploring the solution space. Finally, a cloud-based platform is developed to employ the application of the proposed model in real-world design processes.
