Aseismic ability estimation of school building using predictive data mining models

Abstract

The aseismic ability of buildings is generally analyzed using a nonlinear model. Numerical models are constructed based on the structural configuration and material property of buildings by simulating their stress responses and behaviors to obtain their aseismic ability. This method is complex and time-consuming and should be conducted by professionals. Hence, the aseismic ability of buildings cannot be determined rapidly on a large scale. Additionally, rapidly sequencing and screening the aseismic ability of a large number school buildings to make maintenance and management decisions is extremely difficult. This work adopts predictive data-mining models to determine the relationship between basic design parameters of school buildings and their aseismic ability, and then proposes a best model for predicting the aseismic ability of school buildings. Only basic geometric information of school buildings is needed to estimate quickly their aseismic ability. This prediction model must be able to handle the heavy load of evaluating the aseismic ability of school buildings. The proposed model will help maintenance managers conduct detailed assessments and sequencing of reinforcement work through nonlinear analysis. The proposed model can serve as a reference for disaster prevention in disaster plans and staff rescue during rescue work.