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A generalized VS–N correlation using various regression analysis and genetic algorithm

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Abstract

Shear wave velocity (VS) is an important parameter from geotechnical earthquake engineering perspective. In the absence of the direct measurement of VS, VS–N correlation serves an alternative for evaluating the shear wave velocity (VS) from standard penetration resistance value (SPT-N) at a particular region. In the present study, an attempt has been made to propose generalized VS–N correlation from the globally available correlations for all soil and specific soil type using various regression procedures and genetic algorithm (GA). Various regression procedures along with GA have been used to assess the sensitivity of generalized VS–N correlations in connection with regression methodology. 1D ground response analysis has been carried out at two bore-hole locations in Kolkata city using the VS obtained from generated correlations and site specific response spectra have been generated at those two locations. Finally, performance of some typical buildings have been studied by nonlinear static analysis procedure using those site specific response spectra developed for proposed VS–N relationships for all type of soil and specific type of soil. The performance of the considered typical buildings has been represented in terms of base shear and roof displacement. Chi square (\( \chi^{2} \)) test has also been conducted to check the confidence level of base shear and roof displacement obtained by using various developed VS–N relationships varies with the results obtained using available site specific relationships proposed by Nath (2016), and it reveals that VS–N relationships developed by GA perform better than the VS–N relationships developed by other regression method.

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  1. Civil Engineering Department, Jadavpur University, Kolkata, India

    Amit Shiuly & Narayan Roy

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  1. Amit Shiuly
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  2. Narayan Roy
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Correspondence to Amit Shiuly.

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Shiuly, A., Roy, N. A generalized VS–N correlation using various regression analysis and genetic algorithm. Acta Geod Geophys 53, 479–502 (2018). https://doi.org/10.1007/s40328-018-0220-5

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