Code and data for: Prediction accuracy and optimization outcome diverge with dimensionality: surrogate selection for metaheuristic design of code-checked frames
Code and data accompanying the paper "Prediction accuracy and optimization outcome diverge with dimensionality: surrogate selection for metaheuristic design of code-checked frames" (submitted to Engineering Applications of Artificial Intelligence). The archive contains two self-contained frame design benchmarks (a 30-variable reinforced concrete frame checked to ACI 318 and a 60-variable steel frame checked to AISC 360), four surrogate models (Kriging, a feedforward neural network, XGBoost and radial basis interpolation) behind one interface, a real-coded genetic algorithm with Deb ranking and a repair operator, the results of all 800 optimiz
Resource information
- Format
- zip
- Original repository
- 23919460
- DOI
- 10.5281/zenodo.23919460
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- mit-license
- Last record update
- Nov 5, 2026
About this resource
Code and data accompanying the paper "Prediction accuracy and optimization outcome diverge with dimensionality: surrogate selection for metaheuristic design of code-checked frames" (submitted to Engineering Applications of Artificial Intelligence). The archive contains two self-contained frame design benchmarks (a 30-variable reinforced concrete frame checked to ACI 318 and a 60-variable steel frame checked to AISC 360), four surrogate models (Kriging, a feedforward neural network, XGBoost and radial basis interpolation) behind one interface, a real-coded genetic algorithm with Deb ranking and a repair operator, the results of all 800 optimization runs, and the scripts that regenerate every statistic, table and figure in the paper. See README.md for the file map and reproduction steps.