Prediction of Compression Index Using Regression Analysis of Transformed Variables Method

Bae, Wooseok; Heo, Tae‐Young

doi:10.1080/1064119x.2010.517128

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…Many different correlations based on multiple linear regression analysis have been proposed for determination of compression index (C c ) soil by researchers (Skempton 1944; Terzaghi and Peck 1967; Azzouz et al 1976; Nagaraj and Srinivasa Murthy 1985; Lav and Ansal 2001; Yoon et al 2004; Solanki et al 2008; Dipova and Cangir 2010; Bae and Heo 2011; Akayuli and Ofosu 2013; Lee et al 2015). These studies are generally focused on relationships between the compression index and physical properties of the soils such as the initial void ratio (e 0 ), natural water content (w n ), liquid limit (LL), and plasticity index (PI).…”

Section: Introductionmentioning

confidence: 99%

Prediction of compressibility parameters of the soils using artificial neural network

et al. 2016

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The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

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