Use of ANN, C4.5 and Random Forest Algorithm in the Evaluation of Seismic Soil Liquefaction

Document Type : Regular Article


1 M.Tech Student, Department of Civil Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India

2 Professor, Department of Civil Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India


Liquefaction is one of the disasters caused due to earthquake. In 1999, Chi-Chi, Taiwan, earthquake is an example of liquefaction prone disasters induced due to Mw 7.6 earthquake. This becomes major cause for prediction of the liquefaction in the soil with respect to geotechnical property. In this paper, we have use Artificial Neural Networks (ANN) model based on Resilient Back propagation (Rprop), Decision tree model (DT) and classifier are C 4.5 and Random Forest is done for comparing the performance and evaluation of liquefaction potential based on the obtained field CPT data (Juang et al.,2002) consisting 125 datasets over the simplified procedures that are being traditionally use for the classification of liquefaction of the soil by different researchers. It is observe that Resilient Back propagation Algorithm prediction is 100% whereas C 4.5 algorithm and Random forest Algorithm are 97.6% and 98.4% accurate for the evaluation of seismic soil liquefaction potential.


Main Subjects

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