Trained Random Forests Machine Learning Models for Longitudinal Seismic Response Prediction of Highway Bridges

This page serves as accessible data materials for a Technical Paper submitted to Structural Safety. Wang, X., Yang, D., Ye, A. (202X) "Machine learning-aided deterministic, partially probabilistic, and fully probabilistic seismic resilience assessment methods for highway bridges." (Submitted) Scope Description: It contains a dataset for training machine learning models for longitudinal seismic response predictions of highway bridge portfolios in China, together with the trained random forests-based machine learning models applied to predict the responses based on a user-input new dataset. The scope of highway bridges focuses on seismically designed multi-span continuous RC highway bridges, acting as the most widespread type of bridge in the transportation network in China. Columns are designed as flexure-failure dominated. This excludes bridges prone to shear or flexural-shear failures, ensuring the study remains aligned with modern seismic design principles. Also, soft soil sites are excluded to focus on relatively firm ground. Input Variables [Name, Notation (Unit)]: Column diameter, D (m) Axial load ratio, Ra Column height, H (m) Rebar reinforcement ratio, ρl Number of spans, N Peak ground acceleration, PGA (g) Peak ground velocity, PGV (cm/s) Peak ground displacement, PGD (cm) Spectral acceleration at 1.0s, Sa,1.0 (g) Spectral velocity at 1.0s, Sv,1.0 (cm/s) Housner intensity, HI (cm) Average spectral acceleration, AvgSa (g) Output Variables [Name, Notation (Unit)]: Peak column drift ratio, γp Residual column drift ratio, γr Peak bearing displacement, δp (m) Residual bearing displacement, δr (m) Peak expansion joint displacement, Δp (m) Suggested Platform Versions: Python version: 3.8 H2O version: h2o-3.46.0.6本页面为投稿至《结构安全（Structural Safety）》的学术论文提供可公开获取的数据集支撑材料。 Wang X、Yang D、Ye A（202X）：《机器学习辅助的确定性、部分概率性及全概率性公路桥梁地震韧性评估方法》（已投稿） ### 范围说明 本数据集包含用于训练机器学习模型的相关数据，以实现中国公路桥梁群的纵向地震响应预测；同时附带经训练的基于随机森林（Random Forest）的机器学习模型，可基于用户输入的新数据集完成响应预测。 本次研究聚焦的公路桥梁类型为符合抗震设计要求的多跨连续钢筋混凝土（Reinforced Concrete, RC）公路桥梁——此类桥梁是我国交通网络中分布最广泛的桥型。其桥墩设计以弯曲破坏为控制模式，排除了易发生剪切或弯剪破坏的桥梁，确保研究符合现代抗震设计准则。同时，本数据集剔除了软土地基场景，仅针对相对坚实的地基开展研究。 ### 输入变量 [名称、符号（单位）] 1. 桥墩直径，D（m） 2. 轴压比，Ra 3. 桥墩高度，H（m） 4. 纵筋配筋率，ρ_l 5. 跨数，N 6. 峰值地面加速度（Peak Ground Acceleration, PGA）（g） 7. 峰值地面速度（Peak Ground Velocity, PGV）（cm/s） 8. 峰值地面位移（Peak Ground Displacement, PGD）（cm） 9. 1.0s周期谱加速度（Spectral Acceleration at 1.0s, Sa,1.0）（g） 10. 1.0s周期谱速度（Spectral Velocity at 1.0s, Sv,1.0）（cm/s） 11. 豪斯纳烈度（Housner Intensity, HI）（cm） 12. 平均谱加速度（Average Spectral Acceleration, AvgSa）（g） ### 输出变量 [名称、符号（单位）] 1. 桥墩峰值位移角，γ_p 2. 桥墩残余位移角，γ_r 3. 支座峰值位移，δ_p（m） 4. 支座残余位移，δ_r（m） 5. 伸缩缝峰值位移，Δ_p（m） ### 推荐使用的平台版本 Python版本：3.8 H2O版本：h2o-3.46.0.6