Modeling of Accumulated Energy Ratio (AER) for Estimating LiqueFaction Potential Using Artificial Neural Network (ANN) and Gene Expression Programming (GEP) (using data from Tabriz)

Subject Areas : Structural Engineering

Armin Sahebkaram Alamdari ¹ , Rouzbeh Dabiri ² , Rasoul Jani ³ , Fariba Behrouz Sarand ⁴

1 - Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 - Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
4 - Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Received: 2021-10-02 Accepted : 2022-01-20 Published : 2021-07-01

Keywords: Artificial Neural Network, Tabriz City, Liquefaction potential, Accumulated energy ratio, Genetic expression programming,

Abstract :

Presenting a model specific to the city of Tabriz to estimate the liquefaction potential due to the region's seismicity and the high groundwater level can be effective in dealing with and predicting solutions to deal with this phenomenon. In recent years, the accumulation energy ratio (AER) as a parameter for estimating the liquefaction potential in the energy-based method proposed by Kokusho (2013) has been considered by many researchers. In this research, using perceptron multilayer (MLP) and radial base function (RBF) methods in artificial neural network (ANN) and genetic expression programming (GEP), the accumulation energy ratio using seismic and geotechnical data is modeled for the city of Tabriz. These modeling’s performed by all three methods are well consistent with the outputs. Still, the modeling performed using the Perceptron Multilayer (MLP) method is very compatible with the outputs and can estimate the results with an acceptable percentage. The relationship presented by genetic expression programming (GEP), which is trained with local data, can also yield satisfactory results from estimating the rate of accumulated energy in the study area and provided an independent and accessible relationship trained. With data specific to the study area, there is another advantage.

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