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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221450952500155X |
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| author | Mana Alyami Irfan Ullah Furqan Ahmad Hisham Alabduljabbar |
| author_facet | Mana Alyami Irfan Ullah Furqan Ahmad Hisham Alabduljabbar |
| author_sort | Mana Alyami |
| collection | DOAJ |
| description | 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 (R2) 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. |
| format | Article |
| id | doaj-art-55a7854c5ce747e1994cc4f15a6b97d6 |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-55a7854c5ce747e1994cc4f15a6b97d62025-02-10T04:34:24ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04357Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithmsMana Alyami0Irfan Ullah1Furqan Ahmad2Hisham Alabduljabbar3Department of Civil Engineering, College of Engineering, Najran University, Najran, Saudi Arabia; Corresponding authors.Department of Civil and Transportation Engineering, Hohai University, Nanjing, 210098, ChinaUNHCR, Afghanistan; Corresponding authors.Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaRecent 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 (R2) 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.http://www.sciencedirect.com/science/article/pii/S221450952500155XElectromagnetic radiationShielding effectivenessMetaheuristic algorithmsMachine learningFiber-reinforced mortars |
| spellingShingle | Mana Alyami Irfan Ullah Furqan Ahmad Hisham Alabduljabbar Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms Case Studies in Construction Materials Electromagnetic radiation Shielding effectiveness Metaheuristic algorithms Machine learning Fiber-reinforced mortars |
| title | Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms |
| title_full | Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms |
| title_fullStr | Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms |
| title_full_unstemmed | Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms |
| title_short | Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms |
| title_sort | predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms |
| topic | Electromagnetic radiation Shielding effectiveness Metaheuristic algorithms Machine learning Fiber-reinforced mortars |
| url | http://www.sciencedirect.com/science/article/pii/S221450952500155X |
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