Back close

Seismic Data Analytics for Estimating Seismic Landslide Hazard Using Artificial Accelerograms

Publication Type : Journal Article

Publisher : Advances in Intelligent Systems and Computing book series (AISC,volume 1311)

Source : Advances in Intelligent Systems and Computing book series (AISC,volume 1311)

Url :

Keywords : Artificial accelerogram Seismic data analytics Earthquake-induced landslides Ground motion parameters

Campus : Amritapuri

School : School of Engineering

Department : Electronics and Communication

Year : 2021

Abstract : Accelerograms record energy released by earthquakes as accelerations. Earthquakes and their repercussions such as tsunami and landslides are a huge threat to human life. Earthquake-induced landslides are voluminous and rapid by nature. To critically evaluate the possibility of landslides due to earthquakes, accelerograms recorded at the landslide site can be used to model slope displacement. Large repositories of accelerograms that store data from earthquakes around the world are available in online data repositories and can be accessed open source. However, site-specific data for slope failures during earthquakes is rare and in most cases impractical to be obtained. To address this drawback in strong motion datasets, for the analysis of earthquake-induced landslides, artificial accelerograms that can closely represent the ground motion can be substituted. In this work, a comparison of recorded accelerograms and artificial accelerograms is presented. The ability of the artificial accelerogram to retain the ground motion parameters (GMP) is investigated by generating site-specific accelerograms. Physical and statistical models of earthquake-induced landslides are used as case study to validate the application of generated accelerograms. Results indicate that artificial accelerograms are able to retain most of the GMPs. Their application to the physical and statistical models shows promise in predicting and evaluating future hazards.

Admissions Apply Now