Genetic programming for predicting aseismic abilities of school buildings

In general, the aseismic ability of buildings is analyzed using nonlinear models. To obtain aseismic abilities of buildings, numerical models are constructed based on the structural configuration and material properties of buildings, and their stress responses and behaviors are simulated. This method is complex, time-consuming, and should only be conducted by professionals. In the past, soft computing techniques have been applied in the construction field to predict the particular stress responses and behaviors; however, only a few studies have been made to predict specific properties of entire buildings. In this study, a weighted genetic programming system is developed to construct the relation models between the aseismic capacity of school buildings, and their basic design parameters. This is based on information from the database of school buildings, as well as information regarding the aseismic capacity of school buildings analyzed using complete nonlinear methods. This system can be further applied to predict the aseismic capacity of the school buildings.

► We propose an application of weighted genetic programming on predict aseismic ability of real school buildings.
► We build prediction models to predict aseismic ability of real school buildings.
► The models have better accuracy than it proposed by ANNs and can be formed as a mathematic formulation.
► We select an attribute set which can represent the characteristic of school buildings.