Using empirical correlations and artificial neural network to estimate compressibility of low plasticity clays
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ORIGINAL PAPER
Using empirical correlations and artificial neural network to estimate compressibility of low plasticity clays Davood Akbarimehr 1 & Abolfazl Eslami 1 & Esmail Aflaki 1 & Reza Imam 1 Received: 6 June 2020 / Accepted: 1 November 2020 # Saudi Society for Geosciences 2020
Abstract Consolidation settlement tests are costly and time-consuming, whereas tests for determining soil physical properties can be performed very rapidly and at lower costs. Therefore, it will be very helpful if the soil compression index (CC) can be determined using soil physical parameters. This study investigated correlations between the CC and the physical properties of undisturbed and remolded Tehran clay by performing 125 consolidation tests and through determining the physical properties. Based on the results, the correlations between the CC and dry density (γd), between the CC and initial void ratio (eo), and between the CC and wet density (γw) are valid and have correlation coefficient (R2) of 0.87, 0.87, and 0.89, respectively, for undisturbed Tehran clay. These correlations have been proposed for engineering applications in this area. Furthermore, available empirical correlations were compared with those presented in this study and the results suggested that the low accuracy of some of the available correlations for estimating the CC of Tehran clay soil required accurate evaluations before using them in engineering applications. Moreover, using artificial neural network showed high potential in predicting the CC and have coefficient (R) close to 1. Keywords Artificial neural network . Compression index . Consolidation test . Correlation . Low plasticity clay Settlement . Tehran clay
Abbreviations CC Compression index Ccr Cc of remolded samples Ccu Cc of undisturbed samples LL Liquid limit PI Plasticity index Wn Natural water content e0 Initial void ratio γd Dry density γw Wet density R2 Correlation coefficient in linear regression R Correlation coefficient in artificial neural network ANN Artificial neural network
Responsible Editor: Zeynal Abiddin Erguler * Davood Akbarimehr [email protected] 1
Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
Introduction The study of consolidation settlement in fine-grained soils enjoys considerable significance. Settlement in this soil type due to the increased applied load can be measured using the CC. This parameter is obtained by measuring the slope of the effective stress-void ratio curves determined in consolidation tests (Das 2014). However, consolidation tests are costly and timeconsuming whereas physical properties of soils can be determined rapidly and at lower costs. Therefore, it will be very helpful if the CC can be determined using its physical parameters. Geotechnical properties of a soil depend on its type. Soils from various regions are different in their properties (Akbarimehr and Aflaki 2019b; Perisic et al. 2019; Eslami et al. 2019; Akbarimehr et al. 2019). Given the significance of this parameter, and also t
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