Bibliographic Details
| Title: |
Estimation of compressive strength for spiral stirrup-confined circular concrete column using optimized machine learning with interpretable techniques. |
| Authors: |
Sun, Yang |
| Source: |
Mechanics of Advanced Materials & Structures; 2024, Vol. 31 Issue 28, p10839-10858, 20p |
| Subject Terms: |
Concrete columns, Machine learning, Concrete construction, Graphical user interfaces, Compressive strength, Boosting algorithms |
| Abstract: |
The compressive strength (CS) of a concrete column confined with spiral stirrups is an important indicator for assessing the safety and stability of concrete structures. However, achieving accurate CS estimation for confined concrete remains challenging due to the complex confinement mechanism provided by spiral stirrups. In this study, three robust machine learning (ML) algorithms—support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost)—are employed to predict the CS value of the spiral stirrup-confined circular concrete columns. The hyperparameters of the ML models undergo fine-tuning via Bayesian optimization with 10-fold cross-validation, and the optimized ML models are evaluated for their predictive capabilities. Results show that compared to SVR and RF, XGBoost exhibits more stable generalization performance, achieving an average coefficient of determination (R2) of 0.944 for the 10-fold cross-validation, and demonstrates superior accuracy on the testing dataset with an R2 value of 0.967. To provide insights into the relationship between input features and the output CS value, Individual Conditional Exception (ICE) plots, one/two-dimensional Partial Dependence Plots (PDPs), and Shapley Additive Explanation (SHAP) techniques are utilized to interpret the optimized XGBoost model. Additionally, a friendly online graphical user interface (GUI) has been specially developed based on the optimized XGBoost model to facilitate convenient CS estimation for spiral stirrup-confined circular concrete column. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |