Predictive Analysis of Co-seismic Rock Fall Hazard in Hualien County Taiwan
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Rock fall hazards pose a significant danger to human lives. Being the most abundant among the slope failures in an earthquake event, rock falls are one of the most destructive co-seismic events. A recent earthquake in Taiwan (Mw 6.1) on April 18, 2019, has been analyzed, and artificial accelerograms were generated using SeismoArtif software. In preserving the site properties, the Chi-Chi earthquake accelerogram was used to match the spectral envelope. Data of rock fall during earthquake in the Zhongbu cross island highway in the Hualien County was collected from a dash-cam recording of the event. This rock fall was modeled in 2-D using the ‘Rockfall’ software by Rocscience, and the number of rocks with respect to the rotational energy of the modeled rock was studied. The artificial accelerogram was used as an input to the predictive model, and the results predicted the Newmark’s displacements. It was found that the predicted displacement values were significant enough to trigger the rock fall but the topography as observed by simulation has aided in the propagation of the rock fall.
KeywordsCo-seismic rock fall Newmark’s method SeismoArtif Rockfall 2019
- 1.Jacklitch, C.J. 2016. A geotechnical investigation of the 2013 fatal rockfall in Rockville, Utah. https://etd.ohiolink.edu/!etd.send_file?accession=kent1464978379&disposition=inline.
- 2.Ma, S., C. Xu. 2018. Assessment of co-seismic landslide hazard using the Newmark model and statistical analyses: a case study of the 2013 Lushan, China, Mw6.6 earthquake. Natural Hazards. https://doi.org/10.1007/s11069-018-3548-9.
- 3.Saygili, G., and E.M. Rathje. 2008. Empirical predictive models for earthquake-induced sliding displacements of slopes. Journal of Geotechnical and Geoenvironmental Engineering 134: 790–803. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:6(790).CrossRefGoogle Scholar
- 5.Fan, X., G. Scaringi, Q. Xu, W. Zhan, L. Dai, Y. Li, X. Pei, Q. Yang, R. Huang. 2018. Coseismic landslides triggered by the 8th August 2017 Ms 7.0 Jiuzhaigou earthquake (Sichuan, China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification. Landslides 15, 967–983. https://doi.org/10.1007/s10346-018-0960-x.
- 6.Ii, F., P.D. Calcaterra, G. Pappalardo, P. Sebastiano, P. Zampelli, P.M. Fedi, S. Mineo. 2017. Analysis of rock masses belonging to the Apennine-Maghrebide Orogen by means of in situ and remote methodologies applied to rockfall risk assessment. www.fedoa.unina.it/11456/1/Mineo_Simone_XXIX.pdf.
- 7.Harp, E.L. 2002. Anomalous concentrations of seismically triggered rock falls in Pacoima Canyon: are they caused by highly susceptible slopes or local amplification of seismic shaking? Bulletin of the Seismological Society of America 92: 3180–3189. https://doi.org/10.1785/0120010171.CrossRefGoogle Scholar
- 8.Ku, C.Y. 2014. A 3-D numerical model for assessing rockfall hazard. Disaster Advances 7: 73–77.Google Scholar
- 11.Sepúlveda, S.A., W. Murphy, R.W. Jibson, and D.N. Petley. 2005. Seismically induced rock slope failures resulting from topographic amplification of strong ground motions: the case of Pacoima Canyon, California. Engineering Geology 80: 336–348. https://doi.org/10.1016/j.enggeo.2005.07.004.CrossRefGoogle Scholar
- 12.Jibson, R.W. 2011. Methods for assessing the stability of slopes during earthquakes—a retrospective. Engineering Geology 122, 43–50.Google Scholar
- 14.Wang, G., A. Suemine, F. Zhang, Y. Hata, H. Fukuoka, T. Kamai. 2014. Some fluidized landslides triggered by the 2011 Tohoku Earthquake (Mw 9.0), Japan. Geomorphology 208, 11–21.Google Scholar
- 16.USGS. 2019. Earthquake overview—United States Geological Survey. https://earthquake.usgs.gov/earthquakes/eventpage/us700038c1/executive.
- 18.https://www.rocscience.com/: Rockfall 2019—Rocscience. https://www.rocscience.com/help/rocfall/#t=rocfall%2FGetting_Started.htm.
- 19.Seismosoft. 2018. SeismoArtif, https://www.seismosoft.com/seismoartif.
- 20.Li, L.Ping., S. qu. Sun, S. cai. Li, Q. qing. Zhang, C. Hu, S. shuai. Shi. 2016. Coefficient of restitution and kinetic energy loss of rockfall impacts. KSCE Journal of Civil Engineering 20, 2297–2307. https://doi.org/10.1007/s12205-015-0221-7.
- 25.Chousianitis, K., V. Del Gaudio, N. Sabatakakis, K. Kavoura, G. Drakatos, G.D. Bathrellos, and H.D. Skilodimou. 2016. Assessment of earthquake-induced landslide hazard in Greece: from arias intensity to spatial distribution of slope resistance demand. Bulletin of the Seismological Society of America 106: 174–188. https://doi.org/10.1785/0120150172.CrossRefGoogle Scholar
- 26.Yue, X., S. Wu, Y. Yin, J. Gao, and J. Zheng. 2018. Risk identification of seismic landslides by joint Newmark and RockFall analyst models: a case study of roads affected by the Jiuzhaigou earthquake. International Journal of Disaster Risk Science 9: 392–406. https://doi.org/10.1007/s13753-018-0182-9.CrossRefGoogle Scholar
- 29.Nirmala, V., K. Sreevalsa, S. Aadityan, R. Kaushik. 2016. An investigative study of seismic landslide hazards. https://doi.org/10.2991/rare-16.2016.60.Google Scholar
- 30.Ramesh, M.V., Vasudevan, N. 2012. The deployment of deep-earth sensor probes for landslide detection. Landslides 9, 457–474.Google Scholar