Expression Programming Techniques for Formulation of Structural Engineering Systems

Modeling the real behavior of structural systems is very difficult because of the multivariable dependencies of materials and structural responses. To deal with this complex behavior, simplifying assumptions are commonly incorporated into the development of the conventional methods. This may lead to very large errors. The present study investigates the simulation capabilities of expression programming (EP) techniques by applying them to complex structural engineering problems. Gene expression programming (GEP) and multiexpression programming (MEP) are the employed EP systems. Compared with traditional genetic programming, the EP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. GEP and MEP are substantially useful in deriving empirical models for characterizing the behavior of structural engineering systems by directly extracting the knowledge contained in the experimental data. The problems analyzed herein include the following: (i) prediction of shear strength of reinforced concrete columns and (ii) prediction of hysteretic energy demand in steel moment resisting frames. The results obtained by GEP and MEP are compared with those provided by other equations presented in the literature and found to be more accurate. The new approaches of GEP and MEP overcome the shortcomings of different methods previously presented in the literature for the analysis of structural engineering systems. Contrary to artificial neural networks and many other soft computing tools, GEP and MEP provide reasonably simplified prediction equations. The derived equations can be used for routine design practice. Unlike the conventional methods, GEP and MEP do not require any simplifying assumptions in developing the models.KeywordsData mining, structural engineering, expression programming, prediction