Risk Identification of Seismic Landslides by Joint Newmark and RockFall Analyst Models: A Case Study of Roads Affected by the Jiuzhaigou Earthquake

• Main Authors: Xiliu Yue, Shaohong Wu, Yunhe Yin, Jiangbo Gao, Jingyun Zheng
• Format: Article
• Language: English
• Published: SpringerOpen 2018-09-01
• Series: International Journal of Disaster Risk Science
• Subjects: China, Jiuzhaigou earthquake; Newmark model; Risk identification; RockFall analyst model; Seismic landslide
• Online Access: http://link.springer.com/article/10.1007/s13753-018-0182-9

Abstract Geological disasters are a great threat to people’s lives and property. At present, it is difficult to evaluate quantitatively the cascading effects of regional geological disasters, and the development of new methods for such evaluation is much needed. In this study, the authors have developed a joint procedure that couples the Newmark model and the RockFall Analyst model based on a GIS platform in order to identify the impact of seismic landslides on roads. The new method effectively combines two processes—seismic landslide occurrence probability analysis and mass movement trajectory simulation. The permanent displacement derived from the Newmark model is used to identify potential source areas of landslides. Based on the RockFall Analyst model, the possible impact of mass movement on the roads can be simulated. To verify the reliability of the method, the landslides induced by the 2017 Jiuzhaigou Earthquake were taken as a case study. The results suggest that about 21.37% of the study area is at high risk of seismic landslides, and approximately 3.95 km of road sections are at extremely high risk of large landslides. The simulated area is consistent with the distribution of disasters revealed by post-earthquake remote sensing image interpretation and field investigation in existing studies. This indicates that the procedure, which joins the Newmark and RockFall models, has a high reliability for risk identification and can be applied to seismic landslide risk assessment and prediction in similar areas.