Estimating seismic demand models of a building inventory from nonlinear static analysis using deep learning methods

Probabilistic seismic demand analysis (PSDA) is the most time- and effort-intensive step in risk-based assessment of the built environment. A typical PSDA requires subjecting the structure to a large number of ground motions and performing nonlinear dynamic analysis, where the analysis dimension and effort substantially increase at large-scale assessments such as community-level evaluations. This study presents a deep learning framework to estimate seismic demand models from nonlinear static (i.e., pushover) analysis, which is computationally inexpensive. The proposed architecture leverages an encoder-decoder model with customized training schedules and a loss function capable of determining demand model parameters and error. Furthermore, the framework facilitates the seamless incorporation of structural modeling uncertainties in PSDA. The proposed framework is then applied to a building inventory consisting of 720 concrete frames to examine its generalizability and accuracy. The results show that the deep learning architecture can estimate demand models by an R-2 of 84% using a test-to-train ratio of unity. In addition, the average prediction error is less than 3% and 6% for demand model slope and intercept parameters, respectively, translating into an accurate estimation of fragility functions with a median error of 5.7%, 6.9%, and 6.8% for immediate occupancy, life safety, and collapse prevention damage states. Lastly, the framework can efficiently propagate structural uncertainties into seismic demand models, capturing the implicit relationship of the frames' nonlinear characteristics and resultant fragility functions.