| 논문명 |
형상 변수에 따른 기둥의 최대 지진 변위응답 예측을 위한 기존 머신러닝과 스태킹 앙상블 모델의 성능 비교 / Performance Comparison of Conventional Machine Learning and Stacking Ensemble Models for Predicting Peak Seismic Displacement Response of Columns Considering Geometric Parameters |
| 수록사항 |
한국지진공학회 논문집, Vol.30 No.5(통권 173호) (2026-09) |
| 주제어 |
; Concrete column; Seismic displacement response; Machine learning; Stacking ensemble |
| 요약2 |
This study comparatively analyzes conventional machine learning models and a stacking ensemble model for predicting the peak displacement response of concrete columns under seismic loading. Using geometric parameters, material properties, and ground motion characteristics as inputs, a database of 1,080 cases was constructed via finite-element time-history analysis. Hyperparameter optimization was applied to six conventional machine learning models based on tree-based, kernel-based, and neural network approaches. A stacking ensemble is constructed with Extra Trees and XGBoost as base models and RidgeCV as the meta-model, and its optimal configuration is derived by examining meta-model selection, passthrough application, and the number of cross-validation folds. Predicted peak displacement responses were converted into drift ratios normalized by column height to evaluate predictive performance across models.
The tree-based models show stable performance with a MAPE of 3-4%, while the stacking ensemble achieves the lowest error rate at 2.68% MAPE. Interval-based analysis shows that the stacking ensemble yields lower error rates than conventional machine learning models across a range of geometric parameters, including column height, cross-sectional dimensions, and aspect ratio, confirming stable predictive performance under variations in these parameters. |
