An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns
This study introduces an innovative hybrid predictive model utilizing artificial neural network (ANN) techniques to accurately forecast the load-carrying capacity (Pcc) and confined strain (εcc) of Polyvinyl Chloride – Carbon Fiber Reinforced Plastic (PVC-CFRP) confined concrete columns under axial...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525001318 |
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| author | Li Shang Haytham F. Isleem Saad A. Yehia Rupesh Kumar Tipu Khalil El Hindi |
| author_facet | Li Shang Haytham F. Isleem Saad A. Yehia Rupesh Kumar Tipu Khalil El Hindi |
| author_sort | Li Shang |
| collection | DOAJ |
| description | This study introduces an innovative hybrid predictive model utilizing artificial neural network (ANN) techniques to accurately forecast the load-carrying capacity (Pcc) and confined strain (εcc) of Polyvinyl Chloride – Carbon Fiber Reinforced Plastic (PVC-CFRP) confined concrete columns under axial compression. The use of PVC-CFRP in civil engineering improves durability, strength and stiffness of structural components, and accurate prediction of these properties is needed for design and safety evaluations. Incorporating Random Forest for feature selection and Neural Network trained with the Beetle Antenna Search (BAS) algorithm, the proposed model is more precise and reliable in predicting the system response. Empirical validation was done by training the model on a dataset of 268 data points and the model achieved a test R squared (R2) of 0.971 with lower prediction errors (Root Mean Square Error (RMSE) of 25.05684, Mean Absolute Error (MAE) of 13.18642, Mean Absolute Percentage Error (MAPE) of 0.0178) than existing models. The level of accuracy in the study is high, indicating the robustness of the model and the possibility of using it in its practical engineering context. In addition, the research presents the development of a user interface platform for the easy application of the predictive model, enabling its usability by professionals in the field. The main novelty of this work is the way it tries to bridge the gap between the advancements in machine learning techniques and practical applications in engineering by giving an example of a future innovation in structural engineering analytics. In addition, this model has better predictive accuracy, yet also improves interpretability and usability, which are crucial for improving current design and assessment practice in civil engineering. |
| format | Article |
| id | doaj-art-a33f0dc7dbb3436699cddb48d11f1bb1 |
| institution | Matheson Library |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-a33f0dc7dbb3436699cddb48d11f1bb12025-07-11T04:30:59ZengElsevierEgyptian Informatics Journal1110-86652025-09-0131100738An ANN-based approach for estimating load capacity and strain in FRP confined concrete columnsLi Shang0Haytham F. Isleem1Saad A. Yehia2Rupesh Kumar Tipu3Khalil El Hindi4School of Civil and Hydraulic Engineering, Xichang University, Xichang 615000, ChinaDepartment of Computer Science, University of York, York YO10 5DD, United Kingdom; Corresponding author.Department of Civil Engineering, Higher Institute of Engineering and Technology, Kafrelsheikh, EgyptDepartment of Civil Engineering, School of Engineering & Technology, K. R. Mangalam University, Gurugram, Haryana 122103, IndiaDepartment of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaThis study introduces an innovative hybrid predictive model utilizing artificial neural network (ANN) techniques to accurately forecast the load-carrying capacity (Pcc) and confined strain (εcc) of Polyvinyl Chloride – Carbon Fiber Reinforced Plastic (PVC-CFRP) confined concrete columns under axial compression. The use of PVC-CFRP in civil engineering improves durability, strength and stiffness of structural components, and accurate prediction of these properties is needed for design and safety evaluations. Incorporating Random Forest for feature selection and Neural Network trained with the Beetle Antenna Search (BAS) algorithm, the proposed model is more precise and reliable in predicting the system response. Empirical validation was done by training the model on a dataset of 268 data points and the model achieved a test R squared (R2) of 0.971 with lower prediction errors (Root Mean Square Error (RMSE) of 25.05684, Mean Absolute Error (MAE) of 13.18642, Mean Absolute Percentage Error (MAPE) of 0.0178) than existing models. The level of accuracy in the study is high, indicating the robustness of the model and the possibility of using it in its practical engineering context. In addition, the research presents the development of a user interface platform for the easy application of the predictive model, enabling its usability by professionals in the field. The main novelty of this work is the way it tries to bridge the gap between the advancements in machine learning techniques and practical applications in engineering by giving an example of a future innovation in structural engineering analytics. In addition, this model has better predictive accuracy, yet also improves interpretability and usability, which are crucial for improving current design and assessment practice in civil engineering.http://www.sciencedirect.com/science/article/pii/S1110866525001318PVC-CFRP tubeLoad-carrying capacityConfined concrete columnMachine learning ModelsNN model |
| spellingShingle | Li Shang Haytham F. Isleem Saad A. Yehia Rupesh Kumar Tipu Khalil El Hindi An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns Egyptian Informatics Journal PVC-CFRP tube Load-carrying capacity Confined concrete column Machine learning Models NN model |
| title | An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns |
| title_full | An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns |
| title_fullStr | An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns |
| title_full_unstemmed | An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns |
| title_short | An ANN-based approach for estimating load capacity and strain in FRP confined concrete columns |
| title_sort | ann based approach for estimating load capacity and strain in frp confined concrete columns |
| topic | PVC-CFRP tube Load-carrying capacity Confined concrete column Machine learning Models NN model |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525001318 |
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