UCLA

Earthquake is the most destructive hazard in building design; base-isolations, as one effective method mitigating the earthquake hazard, are widely used in the building design. However, simulating the building response under earthquake using the physical-based model is time-consuming and undesirable. Therefore, several statistical methods (linear regression, weighted least square, the decision tree, random forest, and neural network) are applied to predict building responses based on the characteristics of applied earthquakes. After principal component analysis, the Statistical models' prediction matches the simulation data very well, indicating that it is promising to utilize statistical methods in predicting the critical building response under earthquake. These predictions provide insightful guidance to the designer.