After earthquakes, an accurate and efficient seismic-damage prediction is indispensable for emergency response. Existing methods face the dilemma between accuracy and efficiency. A real-time and accurate seismic-damage prediction method based on machine-learning algorithms and multiple intensity measures (IMs) is proposed here. 48 IMs are used for representing the ground-motion characteristics comprehensively, and the workload of the nonlinear time-history analysis (NLTHA) method is replaced by model training in the non-urgent stage to promote efficiency. Case studies with various buildings prove the accuracy and efficiency of the proposed method, and corresponding key IMs are identified by iterative optimization.

Shear walls are important lateral force-resistant components of tall buildings. Hence, a reliable numerical model that can accurately represent the mechanical characteristics and large deformations of shear walls is critical for realistic collapse simulation of tall buildings. Based on the theory of generalized conforming element, a high-performance quadrilateral flat shell element, NLDKGQ, accounting for the large deformation using the updated Lagrangian formulation, is proposed herein and implemented in OpenSees. The reliability of NLDKGQ is validated using classical benchmark problems and reinforced concrete specimens. In addition, its capability in simulating the collapse of a tall building is also demonstrated.