سال انتشار: ۱۳۹۰

محل انتشار: ششمین کنفرانس بین المللی زلزله شناسی و مهندسی زلزله

تعداد صفحات: ۷

نویسنده(ها):

a Alimoradi – Department of Mining, Petroleum and Geophysics Engineering
h Shahsavani – Shahrood University of Technology, Shahrood, Iran
a Kamkar Rohani – Department of Mining, Petroleum and Geophysics Engineering

چکیده:

Shear wave velocity is an important factor in geotechnical investigations such as subsurface layers’ strength evaluation, site investigation and determination of subsurface profile. One of the indirect non-destructive seismic methods for determination of shear wave velocity profile is Spectral Analysis of Surface Waves (SASW). There is an analytical formula which estimates shear wave velocity from SASW’s results. This formula suffers from some simplicities assumed in the wave propagation media. Existence of noise plus these simplicities decreases the accuracy of the results compare to Down Hole Test (DHT) method as a standard approach. To solve this problem we proposed a procedure based on intelligent inversion of the results of SASW method. In this approach we trained an Artificial Neural Network (ANN) to determine the unknown non-linear relationships between SASW results and the real values of shear wave velocity. The results show that the trained artificial neural network can predict shear wave velocity more accurately than abovementioned formula