頻散曲線評估土層剪力波速 - 類神經網路

The continuous surface wave tests (CSWT) to
access dispersion curves and electronic seismic cone
penetration tests (SCPT) to evaluate shear wave
velocity were performed by this study. Furthermore,
the theoretical dispersion curves are developed by
matrix wave propagation method. Based on theoretical
dispersion curves, field dispersion curves, and shear
wave velocity of soil stratum obtained by SCPT, the
backpropagation neural networks (BPNN) are built
with 80% training data and 20% test data to evaluate
shear wave velocity of soil stratum from field
dispersion curves.
The BPNN used in this study have two hidden
layers of 40 neurons. The training BPNN are with
initial weights and biases created by random numbers,
the goal error of 0.001, the learning rate of 0.1~1, and
training cycles less than 100.
Case study shows that the results of the
mapping of shear wave velocity of soil stratum from
field dispersion curves by BPNN may avoid creating
complicated mechanical models to do inversion
method. Comparisons indicate that the neural network
models have consistence and reliability.