Method for rapidly generating urban damage scenarios under non-uniform ground motion input based on matching algorithms and time history analyses

Due to the rapid urbanization, the built environment becomes more complex and denser, making cities especially vulnerable to strong earthquakes. It is important to simulate seismic disaster scenarios of buildings and critical infrastructures accurately in a reasonable time for the development of urban disaster prevention strategies and decision-making frameworks for post-earthquake emergence rescue. In this study, a novel methodology is established to rapidly generate detailed seismic disaster scenarios that virtually illustrate the damage distribution of buildings in city level without executing time history analysis. The non-uniform distribution of ground motions is taken into account for a large area. The large city is divided into small areas, and a scenario database is generated by performing massive nonlinear time history analyses through the city-level simulation platform recently developed by the authors. The proposed method selects and composes the best matching disaster scenarios by assessing the scenario database within a very short time using matching algorithms. Three matching algorithms, including the single wave matching algorithm, multiple waves splicing matching algorithm and multiple waves combination matching algorithm, are applied to form disaster scenario for any given input ground motion. The performance and effectiveness of the three matching algorithms are examined and a case study on simulation of a large city is presented. The results show that the matching algorithms are all feasible, and the multiple waves splicing matching algorithm is the most efficient. The proposed methodology not only produces reasonably similar results and findings to those obtained by the nonlinear time history analysis directly, but also can reduce the computing time by over 98%. •A methodology is established to rapidly generate detailed seismic disaster scenarios for urban areas.•The scenario is made based on searching the best matching disaster scenarios from a pre-established database.•The non-uniform ground motion input is taken into account for large cities.•Three matching algorithms are presented for identifying the best matching disaster scenarios.•The feasibility of methodology is demonstrated for a large city with 76,794 buildings.