Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques
Abstract Concrete compressive strength (CS) is crucial for ensuring the safety, durability, and performance of structures. So, its precise simulation helps anticipate material behavior under various conditions. Despite a comprehensive experimental investigation of the impact of silica (SiO2) on the...
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Nature Portfolio
2025-06-01
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| author | Hamed Shokrnia Ashkan KhodabandehLou Peyman Hamidi Fedra Ashrafzadeh |
| author_facet | Hamed Shokrnia Ashkan KhodabandehLou Peyman Hamidi Fedra Ashrafzadeh |
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| description | Abstract Concrete compressive strength (CS) is crucial for ensuring the safety, durability, and performance of structures. So, its precise simulation helps anticipate material behavior under various conditions. Despite a comprehensive experimental investigation of the impact of silica (SiO2) on the CS of the fiber-reinforced concrete, its mathematical aspects were not well studied. So, this study integrates the ANFIS (adaptive neuro-fuzzy inference system) and ELM (extreme learning machine) machine learning models with three optimization algorithms, i.e., WCA (water cycle algorithm), PSO (particle swarm optimization), and GWO (grey wolf optimizer) to precisely estimate the CS of fiber-reinforced concrete (FRC) containing SiO2. An experimental database comprising 228 datasets is used to develop the models, compare their accuracy, and select the best one. The database contains information on the volumetric percentage of fibers, sample age, amount of coarse/fine aggregates, water, cement, nano silica, and binder as independent features, while the compressive strength is the target variable. The sensitivity assessment approves that the training and generalization abilities of the ELM and ANFIS models for the CS prediction of FRC are improved by their integration with the GWO algorithm. The best model (i.e., ELM-GWO) predicts the testing datasets with the R2 (coefficient of determination), RMSE (root mean square error), SI (scatter index), RPD (relative percent deviation), and PMARE (percent mean absolute relative error) values of 0.9510, 3.985 MPa, 0.061, 0.8, and 5.421, respectively. |
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| spelling | doaj-art-1ebe5821935c4211ba82ef79cdff74342025-08-20T02:05:48ZengNature PortfolioScientific Reports2045-23222025-06-0115111610.1038/s41598-025-05146-2Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniquesHamed Shokrnia0Ashkan KhodabandehLou1Peyman Hamidi2Fedra Ashrafzadeh3Department of Civil Engineering, Ur.C, Islamic Azad UniversityDepartment of Civil Engineering, Ur.C, Islamic Azad UniversityDepartment of Civil Engineering, Ur.C, Islamic Azad UniversityDepartment of Civil Engineering, Ur.C, Islamic Azad UniversityAbstract Concrete compressive strength (CS) is crucial for ensuring the safety, durability, and performance of structures. So, its precise simulation helps anticipate material behavior under various conditions. Despite a comprehensive experimental investigation of the impact of silica (SiO2) on the CS of the fiber-reinforced concrete, its mathematical aspects were not well studied. So, this study integrates the ANFIS (adaptive neuro-fuzzy inference system) and ELM (extreme learning machine) machine learning models with three optimization algorithms, i.e., WCA (water cycle algorithm), PSO (particle swarm optimization), and GWO (grey wolf optimizer) to precisely estimate the CS of fiber-reinforced concrete (FRC) containing SiO2. An experimental database comprising 228 datasets is used to develop the models, compare their accuracy, and select the best one. The database contains information on the volumetric percentage of fibers, sample age, amount of coarse/fine aggregates, water, cement, nano silica, and binder as independent features, while the compressive strength is the target variable. The sensitivity assessment approves that the training and generalization abilities of the ELM and ANFIS models for the CS prediction of FRC are improved by their integration with the GWO algorithm. The best model (i.e., ELM-GWO) predicts the testing datasets with the R2 (coefficient of determination), RMSE (root mean square error), SI (scatter index), RPD (relative percent deviation), and PMARE (percent mean absolute relative error) values of 0.9510, 3.985 MPa, 0.061, 0.8, and 5.421, respectively.https://doi.org/10.1038/s41598-025-05146-2Fibre-reinforced concreteNano silicaCompressive strength predictionMachine learning modelsGrey wolf optimizer |
| spellingShingle | Hamed Shokrnia Ashkan KhodabandehLou Peyman Hamidi Fedra Ashrafzadeh Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques Scientific Reports Fibre-reinforced concrete Nano silica Compressive strength prediction Machine learning models Grey wolf optimizer |
| title | Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques |
| title_full | Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques |
| title_fullStr | Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques |
| title_full_unstemmed | Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques |
| title_short | Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques |
| title_sort | prediction of compressive strength of fiber reinforced concrete containing silica sio2 based on metaheuristic optimization algorithms and machine learning techniques |
| topic | Fibre-reinforced concrete Nano silica Compressive strength prediction Machine learning models Grey wolf optimizer |
| url | https://doi.org/10.1038/s41598-025-05146-2 |
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