Identification of landslide potential in Gajahmungkur village Semarang using ground shear strain analysis

التفاصيل البيبلوغرافية
العنوان: Identification of landslide potential in Gajahmungkur village Semarang using ground shear strain analysis
المؤلفون: A Z Fikriyah, Khumaedi, N M D Putra
المصدر: Journal of Physics: Conference Series. 1170:012072
بيانات النشر: IOP Publishing, 2019.
سنة النشر: 2019
مصطلحات موضوعية: History, Peak ground acceleration, Data processing, Identification (information), Microseism, Vulnerability index, Shear stress, Geotechnical engineering, Landslide, Point (geometry), Geology, Computer Science Applications, Education
الوصف: The Gajahmungkur village, based on slope and geological structure, are included in the landslide potential category. Research of landslide hazard due to earthquake in weak zone can be done by ground shear strain analysis (GSS). This study aims to provide information related to the potential landslide disaster in the Gajahmungkur village of Semarang city. The data were collected using microseismic method. The research area is 42,000 m2 consisting of 12 points and 3 trajectories. Data processing is done using Software of Geopsy and Surfer to obtain the value of vulnerability index of subsurface ground, maximum ground acceleration value, and ground shear strain value. The result of data analysis shows that the research area at point 9, point 10, and point 12 has the value of vulnerability index which is much higher than the other point so that the value of ground shear strain in all three points is also high. The value of ground shear strain obtained at the order of 10−4 then the dynamic nature that occurs in the research points have a plastic character. This plastic characteristic characterizes the area as a landslide potential.
تدمد: 1742-6596
1742-6588
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::ff24a826f37eb42eb23b5b46504a49cc
https://doi.org/10.1088/1742-6596/1170/1/012072
حقوق: OPEN
رقم الأكسشن: edsair.doi...........ff24a826f37eb42eb23b5b46504a49cc
قاعدة البيانات: OpenAIRE