Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures
Steel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry...
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| Format: | Article |
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Elsevier
2024-10-01
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| Series: | Composites Part C: Open Access |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666682024000987 |
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| author | Mohsin Ali Li Chen Qadir Bux Alias Imran Latif Qureshi Deema Mohammed Alsekait Adil Khan Kiran Arif Muhammad Luqman Diaa Salama Abd Elminaam Amir Hamza Majid Khan |
| author_facet | Mohsin Ali Li Chen Qadir Bux Alias Imran Latif Qureshi Deema Mohammed Alsekait Adil Khan Kiran Arif Muhammad Luqman Diaa Salama Abd Elminaam Amir Hamza Majid Khan |
| author_sort | Mohsin Ali |
| collection | DOAJ |
| description | Steel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimize cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were utilized to develop the models. The models were trained using 70 % of the dataset, with 15 % for validation and 15 % for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 %, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions. |
| format | Article |
| id | doaj-art-c733f1b36a9b4069a07b8ed63b79fbad |
| institution | Kabale University |
| issn | 2666-6820 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Composites Part C: Open Access |
| spelling | doaj-art-c733f1b36a9b4069a07b8ed63b79fbad2024-12-09T04:28:19ZengElsevierComposites Part C: Open Access2666-68202024-10-0115100529Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperaturesMohsin Ali0Li Chen1Qadir Bux Alias Imran Latif Qureshi2Deema Mohammed Alsekait3Adil Khan4Kiran Arif5Muhammad Luqman6Diaa Salama Abd Elminaam7Amir Hamza8Majid Khan9School of Civil Engineering, Southeast University Nanjing, ChinaSchool of Civil Engineering, Southeast University Nanjing, China; Corresponding authors.Department of Civil and Environmental Engineering, College of Engineering and Architecture, University of Nizwa, Birkat-al-Mouz, 616, Nizwa, Oman; Corresponding authors.Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; Corresponding authors.Department of Advanced Civil and Structural Engineering, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UKWestern Caspian University, Baku, AzerbaijanDepartment of Civil Engineering, University of Engineering and Technology, Peshawar, PakistanMEU Research Unit, Middle East University, Amman 11831, Jordan; Jadara Research Center, Jadara University, Irbid, 21110, JordanDepartment of Civil Engineering, University of Engineering and Technology, Peshawar, PakistanDepartment of Civil Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USASteel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimize cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were utilized to develop the models. The models were trained using 70 % of the dataset, with 15 % for validation and 15 % for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 %, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions.http://www.sciencedirect.com/science/article/pii/S2666682024000987SFRCElevated temperatureMachine learningCompressive strengthPredictive model |
| spellingShingle | Mohsin Ali Li Chen Qadir Bux Alias Imran Latif Qureshi Deema Mohammed Alsekait Adil Khan Kiran Arif Muhammad Luqman Diaa Salama Abd Elminaam Amir Hamza Majid Khan Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures Composites Part C: Open Access SFRC Elevated temperature Machine learning Compressive strength Predictive model |
| title | Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures |
| title_full | Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures |
| title_fullStr | Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures |
| title_full_unstemmed | Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures |
| title_short | Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures |
| title_sort | genetic programming based algorithms application in modeling the compressive strength of steel fiber reinforced concrete exposed to elevated temperatures |
| topic | SFRC Elevated temperature Machine learning Compressive strength Predictive model |
| url | http://www.sciencedirect.com/science/article/pii/S2666682024000987 |
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