DNN-metamodeling and fragility estimate of high-rise buildings with outrigger systems subject to seismic loads

This paper proposed deep neural networks (DNNs) for the dynamic response of high-rise buildings with one-outrigger systems under two types of seismic hazards. Using an existing database, the hyperparameters for the architecture of the DNNs are determined finding a trade-off between accuracy and complexity. The performance of the proposed DNNs is compared with two metamodels in the literature, a probabilistic demand model and a kriging metamodel. Partial dependence plot and SHapley Additive exPlanations are used for the explanation of the marginal effect of each feature on the predicted outcome of a neural network from the perspective of global and local agnostic. Considering the uncertainty in the input features, the DNNs are then used to formulate fragility estimates for example high-rise buildings with three types of outrigger systems. The model-agnostic analysis suggests that the DNN targeting the inter-story drift and top acceleration shows extremely high sensitivity to variation in the seismic hazard features, earthquake magnitude, and rupture distance. The fragility curves objectively quantify the reliability of buildings with each of the three outrigger systems and show the effectiveness of damped outrigger systems in reducing fragilities.