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A hybrid approach for computational determination of liquefaction potential of Erzurum City Center based on SPT data using response surface methodology

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Abstract

Liquefaction is one of the seismic-based disasters that has devastating economic and human life consequences. Artificial neural networks (ANN) are one of the computational methods used in liquefaction assessments. A wide range of optimization methods were used to gain the best prediction capability for ANN models. Although response surface methodology (RSM) is a widely used optimization method, a limited number of studies use RSM to optimize ANN models. This study was intended to suggest a computational model for liquefaction potential assessments. Within this scope, an ANN model was coded and optimized using the RSM. Erzurum is one of the seismically active provinces of Turkey as it is located in the East Anatolian Tectonic Structure. However, the liquefaction potential of the city has not been thoroughly examined so far. The geotechnical investigation was performed for Erzurum City Center. The specific objectives of this study are to (1) suggest a new ANN-based cyclic stress ratio (CSR) estimation method, (2) examine the usability of RSM in optimization of ANN models, and (3) perform liquefaction assessments for Erzurum City Center using the suggested method. In this context, 55 boreholes were drilled in various points of the city center. Groundwater level, soil type, and SPT-N values were determined for various depths of the boreholes. Liquefaction assessments were carried out for various earthquake scenarios (M = 6.0, 6.5, 7.0, and 7.5), considering the liquefaction severity index. Locations susceptible to liquefaction were summarized with tables and liquefaction potential maps. Liquefaction assessment maps were drawn using geographic information systems (GIS). As a result of the study, it was seen that the suggested ANN-based algorithm gives accurate results, and RSM can be used to optimize artificial neural nets efficiently.

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Data availability

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Civil Engineering, Engineering Faculty, Bayburt University, 69010, Bayburt, Turkey

    Fatih Yılmaz & Hakan Alper Kamiloğlu

  2. Erzurum Metropolitan Municipality, 25100, Erzurum, Turkey

    Şerafettin Öztürkoğlu

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  1. Fatih Yılmaz
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  2. Şerafettin Öztürkoğlu
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  3. Hakan Alper Kamiloğlu
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Correspondence to Hakan Alper Kamiloğlu.

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Responsible Editor: Zeynal Abiddin Erguler

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Yılmaz, F., Öztürkoğlu, Ş. & Kamiloğlu, H. A hybrid approach for computational determination of liquefaction potential of Erzurum City Center based on SPT data using response surface methodology. Arab J Geosci 15, 95 (2022). https://doi.org/10.1007/s12517-021-09312-4

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