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Steel and Composite Structures Volume 52, Number 2, July 25 2024 , pages 145-163 DOI: https://doi.org/10.12989/scs.2024.52.2.145 |
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Hybrid machine learning with HHO method for estimating ultimate shear strength of both rectangular and circular RC columns |
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Quang-Viet Vu, Van-Thanh Pham, Dai-Nhan Le, Zhengyi Kong, George Papazafeiropoulos and Viet-Ngoc Pham
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| Abstract | ||
| This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHORF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage. | ||
| Key Words | ||
| categorical gradient boosting; harris hawks optimization; hybrid model; machine learning; reinforced concrete columns; ultimate shear strength | ||
| Address | ||
| Quang-Viet Vu:1)Laboratory for Computational Civil Engineering, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam 2)Faculty of Civil Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam Van-Thanh Pham:Faculty of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam Dai-Nhan Le:Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, Vietnam Zhengyi Kong:Institute for Sustainable Built Environment, Heriot-Watt University, Edinburgh, United Kingdom George Papazafeiropoulos:Department of Structural Engineering, School of Civil Engineering, National Technical University of Athens, Zografou, Athens 15780, Greece Viet-Ngoc Pham:Faculty of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam | ||