Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms

Recent studies on carbon fiber-reinforced mortars for electromagnetic interference (EMI) shielding have predominantly relied on practical experiments to investigate the correlation between shielding effectiveness (SE) and design attributes. However, these experiments are resource intensive. Machine learning (ML) models present a faster, cost-effective alternative for simulating outcomes and exploring various scenarios. This study adopts a novel approach by utilizing hybrid models, which offer greater accuracy than individual or ensemble ML models. Specifically, support vector regression (SVR) was combined with three optimization algorithms: firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO) to create hybrid models for estimating the SE of carbon fiber-reinforced mortars. Conventional ML techniques like random forest (RF) and decision tree (DT) were also employed for comparison. A dataset of 346 experimental data sets from existing literature was used to evaluate model performance. The SVR-PSO hybrid model demonstrated superior performance, achieving the highest coefficient of determination (R 2 ) value of 0.994, compared to SVR-FFA (0.964) and SVR-GWO (0.929). Model interpretability methods identified the aspect ratio (AR) as the most influential parameter, showing that shielding effectiveness (SE) increases significantly with fiber content (FC) up to 0.7 %, after which it stabilizes, with a linear correlation between SE and AR. A user-friendly interface was developed for instant SE prediction of carbon fiber reinforced mortar, requiring only essential input parameters.