Evaluation of Uncertainty in Shear-Wave Velocity Based on CPT Records Using the Robust Optimization Method

Hajitaheriha, Mohammad Mahdi; Mola-Abasi, Hossein; Li, Jiliang; Salahi, Maziar; Ataee, Omolbanin

doi:10.22059/ceij.2024.366170.1969

Evaluation of Uncertainty in Shear-Wave Velocity Based on CPT Records Using the Robust Optimization Method

Articles in Press

Document Type : Research Papers

Authors

¹ MUN University

² Department of Civil Engineering, Gonbadekavous University, Gonbadekavous, Iran

³ Assistant Professor, Department of Civil Engineering and Construction Management, California Baptist University, California, U.S

⁴ Professor, Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran

⁵ Assistant Professor of Geology Department of Geography, Faculty of Humanities and Social Sciences University of Mazandaran

10.22059/ceij.2024.366170.1969

Abstract

Shear-wave velocity (Vs) is used to evaluate the soil shear modulus and classify the soil type in pseudo-static analysis. Empirical correlations are developed to relate Vs and cone penetration test (CPT) records. However, uncertainty in the input parameter measurements is always a major concern. Therefore, in this research, a novel method based on robust optimization is used to investigate the effect of such uncertainties. To measure the merits of the suggested method, 407 records were collected and categorized for several soil types. The identification procedure used in this study is based on the robust model of least squares, solved using the interior point technique for second-order cone problems. The uncertainty definition is examined against correlation coefficients for empirical models, and optimum values are determined based on the Frobenius norm of the data points. A diagram for calculating the shear wave velocity considering uncertainties was also presented. This study suggests that the robust method is the best pattern recognition tool for uncertain datasets compared to previous statistical models. Other power models also have good accuracy compared to the polynomial model, but when uncertainty is taken into account, the accuracy of the other models is lower compared to the polynomial model.

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