Dataset and code for: Machine learning prediction of direction-dependent nonlinear pushover capacity for torsionally irregular RC buildings
Description
This dataset supports the paper "Machine learning prediction of direction-dependent nonlinear pushover capacity for torsionally irregular RC buildings" (Journal of Building Engineering). It contains the OpenSeesPy models, the generated building population and its multi-directional pushover capacity dataset, and the machine-learning training and evaluation code needed to reproduce all reported results and figures. A population of 253 low-rise reinforced-concrete moment frames is generated by Monte Carlo sampling of design-stage parameters. Torsional (plan) irregularity is introduced through a continuous stiffness eccentricity, produced by grading the square column depths across the plan while the floor mass is kept uniform. Each frame is modelled in three dimensions in OpenSeesPy (force-based elements with concentrated Gauss-Radau fibre hinges, rigid diaphragms, P-Delta) and analysed by nonlinear static (pushover) analysis at 36 loading directions (0-360 degrees in 10-degree steps). Each base-shear- roof-drift curve is reduced to a peak-bounded equal-energy bilinear backbone (V_y, d_y, V_max, d_max). A supervised multi-output regression maps design-stage features (geometry, materials, reinforcement, member sizes, eccentricities e_x/e_y, loading angle, and physics-informed angle-eccentricity interaction terms) to the four backbone parameters. Nine regression algorithms are benchmarked under leave-building-out cross-validation, with CatBoost the most accurate. Contents: - generate_dataset.py : OpenSeesPy model generation and multi-directional pushover; produces the building population and the per-direction capacity dataset. - dataset_buildings.csv / dataset_curves.csv : the generated building parameters and the per-direction capacity/backbone data used for training and evaluation. - param_backbone.py, benchmark.py : feature construction, the nine ML models, the two- stage model, leave-building-out cross-validation, and all reported metrics. - make_figures.py : reproduces the manuscript figures from the dataset and models. - README.txt : file descriptions, software dependencies and versions, and step-by-step instructions to reproduce the results. Software: Python 3.x with OpenSeesPy, NumPy, pandas, scikit-learn, XGBoost, LightGBM, CatBoost, and Matplotlib. Exact versions are listed in README.txt. These materials allow full reproduction of the dataset, the trained surrogates, the accuracy metrics (overall R2 = 0.95, within-building directional R2 = 0.91), and the figures reported in the article.