Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering

<table class="table-group" id="tab9"><tr><td><table class="table"><tr><td class="thead-hr" colspan="8"><hr/></td></tr><tr class="thead"><td class="align_left">Coefficient</td><td class="align_center">FC</td><td class="align_center">D50</td><td class="align_center">PGA</td><td class="align_center">rd</td><td class="align_center">CSR</td><td class="align_center">(<i>N</i><sub>1</sub>)<sub>60</sub></td><td class="align_center">Intercept</td></tr><tr><td class="thead-hr" colspan="8"><hr/></td></tr><tr><td class="align_left">Value</td><td class="align_center">−0.0045</td><td class="align_center">−0.0089</td><td class="align_center">0.2160</td><td class="align_center">0.4136</td><td class="align_center">1.2103</td><td class="align_center">−0.0364</td><td class="align_center">0.451</td></tr><tr class="table-tr"><td colspan="8"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The optimal coefficients of the MVR model.</div>

Computational Intelligence and Neuroscience

tab9

Table 9

Table 9: Application of Feedforward Neural Network and SPT Results in the Estimation of Seismic Soil Liquefaction Triggering 