Machine-Learning-Based Analysis of Internal Forces in Reinforced Concrete Conical and Cylindrical Tanks Under Hydrostatic Pressure Considering Material Nonlinearity
Reinforced concrete (RC) tanks are essential for storing liquids and bulk materials across various industries. However, simplified analytical methods fall short in providing an accurate analysis, while traditional methods, such as finite element modeling, can be computationally intensive and time-co...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/5/779 |
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| Summary: | Reinforced concrete (RC) tanks are essential for storing liquids and bulk materials across various industries. However, simplified analytical methods fall short in providing an accurate analysis, while traditional methods, such as finite element modeling, can be computationally intensive and time-consuming, especially when dealing with nonlinear material properties and complex geometries, like conical and cylindrical shapes. This highlights the need for a more efficient and simplified analysis approach. Accordingly, the present paper introduces a machine learning (ML) framework as an effective predictive tool for RC conical and cylindrical tanks under hydrostatic pressure. Data from 320 RC conical and cylindrical water tanks, previously analyzed using finite element modeling, were used to train and test various ML models, considering geometrical and material nonlinearities. Four machine learning models—decision trees, random forests, gradient boosting, and extreme gradient boosting—were utilized to predict critical internal forces, including the maximum ring tension force, maximum meridional moment, and maximum meridional axial force. The accuracy of each model was evaluated using different statistical measures. To improve model interpretability and identify key predictors, feature importance techniques were employed to rank the significance of each input variable to the predictions. Furthermore, Accumulated Local Effects (ALE) plots were utilized to visualize the relationships between model inputs and outputs, providing a clearer understanding of the inner workings of the ML models. The combined use of feature importance and ALE plots enhances model transparency by illustrating how specific features contribute to the predictions, thereby supporting the informed application of ML in the structural design and analysis of RC tanks. Ultimately, the framework presented in this study aims to promote the practical application of machine learning in structural engineering, contributing to simpler, more efficient, and accurate analysis and design processes for RC water tanks. |
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| ISSN: | 2075-5309 |