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