Empirical correlations for the compression index of Irish soft soils

McCabe, Bryan A.; Sheil, Brian B.; Long, Michael; Buggy, Fintan J.; Farrell, Eric R.

doi:10.1680/geng.13.00116

Cited by 26 publications

(13 citation statements)

References 14 publications

(14 reference statements)

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“…Singh and Noor [6] were the two Indian researchers who conducted similar study to the one executed over here. This study was carried out by many other researchers with the difference of region and soil type [8][9][10][11][12].…”

Section: Selection Of Best Fit Correlation For Compression Index Of Smentioning

confidence: 99%

Selection of Best Fit Correlation for Compression Index of Soil from District Bannu

Rahman

Sirajuddin

Khan

et al. 2019

Mehran Univ. res. j. eng. technol.

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Geotechnical investigation is one of the major steps in the safe and economic design of foundation for any structure. This requires some soil parameters to be established. CI (Compression Index) is one of these parameters. The stability of a foundation is not fully justified as long as the magnitude of settlement is unknown. The problem arises when the value of CI is needed badly and a consolidation test is set up for this purpose which consumes almost 3 days for its completion provided that the assembly would not be disturbed. Little amount of disturbance can cause serious error in further calculations and plotting of data. Undisturbed soil sample is the primary demand of consolidation test which is a tough job for a person having not been experienced very much in the fieldwork. In order to overcome these complexities, the researchers have established various correlations between compression index and some basic soil properties like Atterberg limits, void ratio, natural moisture content and OMC (Open Moisture Content), as they are simple to be determined and takes small amount of time. This research has been conducted on soil samples collected from a single spot at an interval of 5ft depth for the sake of validation of a suitable correlation for Bannu District, KPK (Khyber Pakhtunkhwa), Pakistan so that the determination of compression index becomes simple and convenient.

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Section: Selection Of Best Fit Correlation For Compression Index Of Smentioning

confidence: 99%

Selection of Best Fit Correlation for Compression Index of Soil from District Bannu

Rahman

Sirajuddin

Khan

et al. 2019

Mehran Univ. res. j. eng. technol.

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“…Many researchers have published empirical correlations estimating C C from soil index properties around the world (e.g. Terzaghi & Peck, 1967;Azzouz et al, 1976;Ozer et al, 2008;Kalantary & Kordnaeij, 2012;McCabe et al, 2014;and Kootahi & Moradi, 2016) and for Brazilian soft soils (Futai et al, 2008;Coutinho & Bello, 2014;Baroni & Almeida, 2017). However, empirical correlations may not be applied to soils elsewhere without consideration of soil origin, and the multiplicity of existing empirical correlations indicates the need of evaluation criteria for their use.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compression Index of Soft Soils from the Brazilian Coast Using Artificial Neural Networks and Empirical Correlations

Filho¹,

Totola²,

Bicalho³

et al. 2020

S&R

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This paper aims to explore the potential use of artificial neural networks (ANNs) to predict the compression index (C C ) of soft soils from the Brazilian coast. Results from 225 standard consolidation (oedometer) tests and the corresponding soil index properties (i.e., initial void ratio, natural water content and Atterberg limits) of a wide variety of fine-grained soils reported in the literature were compiled and investigated herein. The ANN prediction performance is compared with linear empirical correlations created from the database investigated. In addition, correlations presented in the literature are also used and evaluated through different statistical techniques. Overall, for the organized dataset, the ANN outperformed the empirical correlations, highlighting the fragility and limitations of single and multiple variable linear empirical correlations.

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“…Thus, empirical correlations can provide fast and cost-effective means of parameter estimation using simple tests [4]. Most of these correlations were derived from data fitting of measurements at specific site conditions that may cause large deviation if used for other sites [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Statistical Evaluation of Geotechnical Correlations

Daoud

Kasama²,

Saleh

et al. 2016

geomate

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Geotechnical correlations are widely used because it can provide fast and cost-effective means of parameter estimation using simple tests. Most of the current correlations were derived from data fitting of measurements for specific site conditions and need to be evaluated before used in other sites. During the correlation development, the deviation of the correlation from the measured values was evaluated using simple statistical measures. A better method of correlation evaluation may reduce the overall deviation of the geotechnical parameter. Amended Theil Inequality Coefficient (ATIC) is proposed as an evaluation tool because it has the advantage that it considers both position and trend conformities between observed and correlated values. To address the efficiency and rationality of ATIC, evaluation of, 92 compression index correlations by ATIC and different statistical measures was carried out. Comparison between the results showed that ATIC is efficient in assessing the best and worst correlations and it can be considered a good tool for correlations evaluation.

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Empirical correlations for the compression index of Irish soft soils

Cited by 26 publications

References 14 publications

Selection of Best Fit Correlation for Compression Index of Soil from District Bannu

Selection of Best Fit Correlation for Compression Index of Soil from District Bannu

Prediction of Compression Index of Soft Soils from the Brazilian Coast Using Artificial Neural Networks and Empirical Correlations

Statistical Evaluation of Geotechnical Correlations

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