PEAK GROUND ACCELERATIONPREDICTION FOR CRITICAL AFTERSHOCKS

سال انتشار: 1398
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 446

فایل این مقاله در 5 صفحه با فرمت PDF قابل دریافت می باشد

این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

SEE08_017

تاریخ نمایه سازی: 27 خرداد 1399

چکیده مقاله:

This paper proposes a methodology using learning abilities of artificial neural networks in order to predict the peak ground acceleration of critical aftershocks based on the features of successive earthquakes. At first, a set of recorded consecutive earthquakes which has been contained critical main shocks and aftershocks is selected based on effective peak acceleration (EPA) from PEER and USGS . In the following, the idealized multilayer artificial neural networks were designed and trained to estimate the peak ground acceleration of critical aftershocks. In this regard, two -layer feed-forward (MLFF) neural networks are used. The results indicate that the networks have learned to generalize the unseen information very well and reflect good precision in the simulation of the peak ground acceleration of critical aftershocks.

نویسندگان

Elham Rajabi

Postdoctoral Fellow, School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran ۱۶۸۴۶, Iran

Gholamreza Ghodrati Amiri

Professor, School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran ۱۶۸۴۶, Iran

Vida Ghasemi

Research Assistant, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran