China Safety Science Journal ›› 2022, Vol. 32 ›› Issue (9): 126-136.doi: 10.16265/j.cnki.issn1003-3033.2022.09.2808

• Technology and engineering of disaster prevention and mitigation • Previous Articles     Next Articles

Review on regional seismic analysis software

CHEN Yongqiang(), WU Zhipeng, YANG Xiangjuan, CHEN Pu, SUN Shuli   

  1. College of Engineering, Peking University, Beijing 100871, China
  • Received:2022-03-14 Revised:2022-07-11 Online:2022-10-19 Published:2023-03-28

Abstract:

In order to strengthen the capabilities of disaster prevention, mitigation, disaster resistance, and disaster relief, it is necessary to study the technology of rapid calculation of earthquake and rapid information reporting, and realize the rapid analysis of the earthquake response of the structure, which provides an important guarantee for public safety rescue. Firstly, seismic analysis software's current status and development trend were introduced, and the main commercial and open source software widely used in China and abroad were systematically sorted out. Then, the self-developed regional seismic dynamics numerical simulation platform EQSDA(Earthquake Simulation and Damage Analysis) and its four major modules were explained in detail, i.e., equivalent source restoration, ground seismic response analysis, community building seismic response analysis, and progressive damage analysis of building structures. The theory and techniques used in each module were illustrated, respectively. Functions of each module were demonstrated through calculation examples. The results show that the advanced seismic wave numerical simulation method combined with machine learning technology can be used to carry out the numerical simulation research of earthquakes and strong ground motion, so the full-chain analysis from the source analysis to the earthquake simulation calculation and the building structure failure simulation can be realized more accurately.

Key words: regional seismic, simulation software, public security, fast calculation, machine learning