Earthquakes and Structures

  • 제10권6호
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  • Pages.1391-1404
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  • 2016
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Seismicity of Peninsular Malaysia due to intraplate and far field sources

  • Loi, Daniel W. (School of Engineering, Monash University Malaysia) ;
  • Raghunandan, Mavinakere E. (School of Engineering, Monash University Malaysia) ;
  • Swamy, Varghese (School of Engineering, Monash University Malaysia)
  • 투고 : 2016.01.22
  • 심사 : 2016.05.11
  • 발행 : 2016.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Peninsular Malaysia lying on the stable Sunda Plate has traditionally been considered safe with low to moderate seismicity. However, far field Sumatran mega-earthquakes have been shown to be capable of triggering ground motions felt in high rise structures in the major Malaysian cities while seismic impact from local earthquakes of moment magnitude 3.8 have reportedly induced nominal structural damages to nearby buildings. This paper presents an overview of the recent seismic activities in and around Peninsular Malaysia with reference to prominent earthquakes generated by far field interplate and local intraplate sources. Records of ground motion data and seismic hazard assessment (SHA) results available in the literature have been analyzed and discussed. The peak ground acceleration (PGA) values from historical records for few local intraplate events were observed to be higher than those for the events from Sumatran Subduction Zone. This clearly points to the need for a detailed and comprehensive SHA incorporating both far field and local sources. Such an analysis would contribute the knowledge required for secure and reliable infrastructure design and safeguard the Malaysian people and economy.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Higher Education Malaysia

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피인용 문헌

  1. Revisiting seismic hazard assessment for Peninsular Malaysia using deterministic and probabilistic approaches vol.18, pp.9, 2016, https://doi.org/10.5194/nhess-18-2387-2018
  2. Real-time seismic structural response prediction system based on support vector machine vol.18, pp.2, 2016, https://doi.org/10.12989/eas.2020.18.2.163