Evaluation of compression index of marine fine-grained soils by the use of index tests

Kootahi, Karim; Moradi, Gholam

doi:10.1080/1064119x.2016.1213775

Cited by 24 publications

(13 citation statements)

References 60 publications

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“…However, subsequent studies showed that using the initial void ratio e 0 as the predictor leads to a higher R 2 value (coefficient of determination) (e.g. Azzouz, Krizek, and Corotis 1976;Al-Khafaji and Andersland 1992;Kootahi and Moradi 2017). Similarly, w n is associated with high R 2 , although Al-Khafaji and Andersland (1992) pointed out that many studies have reported w n values defined for partially saturated soil specimens (rather than fully saturated), which in turn can lead to weaker model accuracy.…”

Section: Existing Transformation Models For Compressibilitymentioning

confidence: 99%

“…Many researchers recommend using a two-predictor model with void ratio e 0 (or w n ) combined with w L (or PI) to account for both the structure and mineralogical effects on clay compressibility (e.g. Al-Khafaji and Andersland 1992;Kootahi and Moradi 2017). That is, the mineralogical composition and particle-surface characteristics can be represented by intrinsic index properties such as w L or PI, whereas the physical properties (e 0 and w n ) represent the structure induced by the sedimentation environment.…”

Section: Existing Transformation Models For Compressibilitymentioning

confidence: 99%

“…the oedometer test result). Table 1 also shows the corresponding soil property statistics for a global database of marine fine-grained soils compiled by Kootahi and Moradi (2017). These databases are also compared in Figure 6.…”

Section: Collected Multivariate Datamentioning

confidence: 99%

“…Consequently, many researchers have presented empirical regression equations (i.e. transformation models) to estimate C c from other properties such as water content (w n ) or liquid limit (w L ) (Skempton 1944;Helenelund 1951;Hough 1957;Moran and Mueser 1958;Nishida 1956;Cozzolino 1961;Terzaghi and Peck 1967;Elnaggar and Krizek 1970;Azzouz, Krizek, and Corotis 1976;Ogawa and Matsumoto (1978); Mayne 1980;Koppula 1981;Saarelainen 1981;Al-Khafaji and Andersland 1992;Kootahi and Moradi 2017;Park and Lee 2011;Di Buò et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Transformation models for the compressibility properties of Finnish clays using a multivariate database

Löfman

Korkiala-Tanttu

2021

Georisk: Assessment and Management of Risk for Engineered Syste

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The compression index obtained from an oedometer test is often used to estimate the settlements of clayey subsoil, but compressibility parameters are rarely available during the preliminary geotechnical design phase. Various empirical correlations linking compressibility to other properties such as water content have been proposed. However, as Scandinavian clays are soft and exhibit greater compressibility, the existing transformation models for compressibility can be biased when applied to Finnish clays. This paper compiles a partial multivariate database of Finnish clayey soils and demonstrates that the existing transformation models tend to underestimate the compressibility of Finnish clays. The new transformation models are constructed by means of a 2-degree polynomial regression applied to the natural logarithms of the soil properties. Finally, the transformation uncertainties are quantified via the standard deviation of errors and the coefficient of variation. The best predictors for the compressibility of Finnish clayey soils were found to be the void ratio and water content. When the void ratio was combined with a secondary predictor, such as the ratio between undrained shear strength and preconsolidation pressure or plastic limit, the transformation uncertainty decreased notably.

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Section: Existing Transformation Models For Compressibilitymentioning

confidence: 99%

Section: Existing Transformation Models For Compressibilitymentioning

confidence: 99%

Section: Collected Multivariate Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Löfman

Korkiala-Tanttu

2021

Georisk: Assessment and Management of Risk for Engineered Syste

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“…The widely used model performance measures in geotechnical engineering are the coefficient of determination (R 2 ), coefficient of efficiency (E), and the ranking index (RI). Coefficient of efficiency, proposed by Nash andSutcliffe in 1970 (Nash andSutcliffe 1970), has several advantages over R 2 (see, e.g., Oommen and Baise 2010) and should be used instead of R 2 in assessing model performances (Kootahi and Moradi 2017). Ranking index, proposed by Briaud andTucker in 1988 (Briaud andTucker 1988), is an overall index of model performance and takes into account both the accuracy and precision of a model to assess its ability in providing acceptable predictions.…”

Section: The Authors Provided Brief Summaries (Inmentioning

confidence: 99%

Discussion of “Accuracy of determining pre-consolidation pressure from laboratory tests”

Kootahi

2017

Can. Geotech. J.

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The authors (Umar and Sadrekarimi 2017) present results from an interesting experimental study to validate the accuracy of different methods available for interpreting pre-consolidation pressure ( p ) from oedometer testing. For this purpose, laboratory oedometer tests were performed on block samples taken from three Canadian clays and specimens were subjected to cycles of one-dimensional compression loading and unloading.The authors are to be commended for providing a high-quality database of laboratory consolidation tests. The discusser would, however, like to raise the following points in relation to the paper.1. The authors provided brief summaries (in Table 1) for the 11 graphical methods of interpreting pre-consolidation pressure, one of which was referred to as "Boone (2010)". Boone's method, however, has been oversimplified by the authors so that the method described in the paper does not match the instructions given by Boone (2010). More specifically, according to Boone (2010), p is the intersection point obtained from the following two lines: (i) a line coincident with C cmax (maximum value of the slope of the virgin compression segment of the void ratio (e)-log( v ) curve) and (ii) a line parallel to C r (average slope of the unload-reload cycle of the recompression segment of the e-log( v ) curve) passing through the point ( v0 , e v0 ) where v0 and e v0 are the in situ vertical effective stress and void ratio at in situ vertical effective stress, respectively. The method described and used by the authors (as the intersection of tangent lines to the recompression and virgin compression segments of the e-log( v ) curve) seems to be appearing for the first time in the literature in Zeevaert (1973) and thus it will be referred to as " Zeevaert's (1973) (2005), intercomparisons of average model-performance error should be based on the mean absolute error (MAE). The overall accuracy of models is usually assessed using model performance measures that quantify the agreement along the identity line (perfect prediction line). The widely used model performance measures in geotechnical engineering are the coefficient of determination (R 2 ), coefficient of efficiency (E), and the ranking index (RI). Coefficient of efficiency, proposed by Nash and Sutcliffe in 1970 (Nash and Sutcliffe 1970), has several advantages over R 2 (see, e.g., Oommen and Baise 2010) and should be used instead of R 2 in assessing model performances (Kootahi and Moradi 2017). Ranking index, proposed by Briaud andTucker in 1988 (Briaud andTucker 1988), is an overall index of model performance and takes into account both the accuracy and precision of a model to assess its ability in providing acceptable predictions. These measures are defined as follows:where n is the total number of measurements, P i is the predicted value, M i is the measured value, M is the mean measured value, is the mean of the series of analyzed data, and is the standard deviation of the series of analyzed data. The range of E values lies between −∞ and 1 with a valu...

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Validation of Compression Index Approximations Using Soil Liquid Limit

Al‐Khafaji

Buehler

Druszkowski

2018

Contemporary Issues in Soil Mechanics

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Evaluation of compression index of marine fine-grained soils by the use of index tests

Cited by 24 publications

References 60 publications

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Discussion of “Accuracy of determining pre-consolidation pressure from laboratory tests”

Validation of Compression Index Approximations Using Soil Liquid Limit

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