Energy-Based Estimation of Soil Liquefaction Potential Using GMDH Algorithm

Abstract

Reliable determination of seismic liquefaction potential of soil is an important obligation in earthquake engineering. In this study, neuro-fuzzy group method of data handling (NF-GMDH) was employed for prediction of strain energy required to induce liquefaction. The NF-GMDH-based model was developed using particle swarm optimization. A wide-ranging database of soil element tests was used to develop an advanced model, capable of predicting soil liquefaction resistance accurately. Input variables of the model were chosen based on the previous studies on the liquefaction potential assessment. Results of geotechnical centrifuge tests were also involved during the training process for adequate generalization of the proposed model for future predictions. A parametric analysis was then performed to evaluate sensitivity of the proposed model to variations of the influencing parameters. A comparison between performance of the developed model and previously recommended relationships was done. The results clearly demonstrate that the proposed model, which was derived based on laboratory results, can be successfully utilized for strain energy-based estimation of liquefaction potential.