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Computers and Concrete Volume 33, Number 6, June 2024 , pages 739-754 DOI: https://doi.org/10.12989/cac.2024.33.6.739 |
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Axial load prediction in double-skinned profiled steel composite walls using machine learning |
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G. Muthumari G and P. Vincent
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| Abstract | ||
| This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering. | ||
| Key Words | ||
| artificial neural network; axial load capacity; double-skinned profiled steel sheet composite walls; hybrid machine learning; machine learning | ||
| Address | ||
| G. Muthumari G: Department of Civil Engineering, Mohamed Sathak Engineering College, Kilakarai, Tamil Nadu, India P. Vincent: Department of Civil Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India | ||