The resilience of cities has received worldwide attention. An accurate and rapid assessment of seismic damage, economic loss, and post-event repair time can provide an important reference for emergency rescue and post-earthquake recovery. Based on city-scale nonlinear time-history analysis (THA) and regional seismic loss prediction, a real-time city-scale time-history analysis method is proposed in this work. In this method, the actual ground motion records obtained from seismic stations are input into the building models of the earthquake-stricken area, and the nonlinear time-history analysis of these models is subsequently performed using a high-performance computing platform. The seismic damage to the buildings in the target region subjected to this earthquake is evaluated according to the analysis results. The economic loss and repair time of the earthquake-stricken areas are calculated using the engineering demand parameters obtained from the time-history analysis. A program named, “Real-time Earthquake Damage Assessment using City-scale Time-history analysis” (“RED-ACT” for short) was developed to automatically implement the above workflow. The method proposed in this work has been applied in many earthquake events, and provides a useful reference for scientific decision making for earthquake disaster relief, which is of great significance to enhancing the resilience of earthquake-stricken areas.

Computational fluid dynamics (CFD) simulation is a core component of wind engineering assessment for urban planning and architecture. CFD simulations require clean and low-complexity models. Existing modeling methods rely on static data from geographic information systems along with manual efforts. They are extraordinarily time-consuming and have difficulties accurately incorporating the up-to-date information of a target area into the flow model. This paper proposes an automated simulation framework with superior modeling efficiency and accuracy. The framework adopts aerial point clouds and an integrated two-dimensional and three-dimensional (3D) deep learning technique, with four operational modules: data acquisition and preprocessing, point cloud segmentation based on deep learning, geometric 3D reconstruction, and CFD simulation. The advantages of the framework are demonstrated through a case study of a local area in Shenzhen, China.