Modeling the amplification ratio of sandy soils using two methods of neural network and gene

When seismic waves pass through alluvial layers, the seismic wave amplitude increases significantly in some periods, which is known as site amplification. In this case, it can be analyzed with an analytical model of the surface response spectrum Estimates of the input response spectrum. This behavior is essential in assessing the seismic performance of the structures and vital arteries. In this paper, we investigate this effect on different layers of sand with different thicknesses for Urmia city, a metropolitan area in the North West of Iran and an earthquake prone region. 120 geotechnical boreholes were drilled in sandy layers of different thicknesses in the central and northern parts of the city. The analysis simulated artificial acceleration based on seismic hazard analysis results. By observing the spectral accelerations for different periods at the earth’s surface the spectral acceleration magnitudes of the seams were shown for different periods. Using a neural network and genetic algorithm, these coefficients were modelled. Using the evolutionary algorithm of gene expression programming, the mathematical relation was expressed in terms of sand layer thickness and different periods. However, the results obtained from the artificial neural network using the correlation coefficient and root mean square yielded more accurate results than the gene expression programming. In conclusion, the results show that by increasing the thickness of the sand layers, the amplification ratio also increases considerably for some periods. Using the modeling results, we can estimate the amplification of the sandy soils of Urmia city with different thicknesses for variables up to 4 seconds.