A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

作者：Aminian Pejman; Javid Mohamad Reza; Asghari Abazar; Gandomi Amir Hossein; Esmaeili Milad Arab^*

来源：Neural Computing & Applications, 2011, 20(8): 1321-1332.

DOI：10.1007/s00521-011-0689-0

• Base shear
• Steel frame structures
• Genetic programming
• Simulated annealing
• Nonlinear modeling

This study presents a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. The base shear of steel frames was formulated in terms of the number of bays, number of storey, soil type, and situation of braced or unbraced. A classical GP model was developed to benchmark the GP/SA model. The comprehensive database used for the development of the correlations was obtained from finite element analysis. A parametric analysis was carried out to evaluate the sensitivity of the base shear to the variation of the influencing parameters. The GP/SA and classical GP correlations provide a better prediction performance than the widely used UBC code and a neural network-based model found in the literature. The developed correlations may be used as quick checks on solutions developed by deterministic analyses.

• 出版日期2011-11