| 수록사항 |
한국공간구조학회지 , Vol. 25, No. 4 (통권 102호)(2025-12) |
| 주제어 |
; Inter-stroy drift ratio; Strain-derived damage image; Convolutional neural network |
| 요약2 |
Rapid post-earthquake retrofit decisions require reliable estimates of interstory drift ratio. Conventional field practices either depend on instrumented measurements constrained by sparse sensor coverage or rely on qualitative expert judgment.
This study aims to develop a CNN-based interstory drift ratio prediction method for reinforced concrete columns using strain-derived damage images. Reinforced concrete columns are modeled and analyzed in OpenSees to obtain strains and displacements. Strain fields are converted into strain-derived damage images through threshold-based staging that encodes discrete damage states. Structural parameters are concatenated to the damage image by adding fixed-value columns so the network can read structural context in a single two-dimensional input. We design systematic comparisons to isolate the benefit of structural information and section coverage. First, models without structural parameters are trained. Second, single-parameter variants are trained where only one attribute is provided. Third, full-parameter models include all attributes. For each setting, both single-section and multi-section inputs are evaluated. Samples are split by case and then divided 80/20 into training and validation sets. Model performance is reported using RMSE, MAE, and R-squared. The proposed approach achieves accurate inter-story drift ratio prediction overall, with improved performance when all structural parameters and multi-section inputs are used. |
