Index Test Method for Estimating the Effective Preconsolidation Stress in Clay Deposits

Kootahi, Karim; Mayne, Paul W.

doi:10.1061/(asce)gt.1943-5606.0001519

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…In addition, in the studies of basic properties, ignoring scale-dependence will make the experimental results deviate from the practical engineering [56]. Considering scale-dependence in the establishment of the model, the prediction results of the model will be closer to the reality and the generalization ability will be better [57][58][59][60]. But the size effect is not taken into account in this study, which may affect the generalization ability of the model.…”

Section: Model Performance Comparisonmentioning

confidence: 99%

Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

et al. 2020

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With the increase in transportation emissions, road diseases in the saline soil area of Jilin Province have become a problem that requires serious attention. In order to improve the subgrade performance, the structural yield strength (SYS) of remolded soil and its factor sensitivity are investigated in this study. Saline soils in Western Jilin are structural in the sense that the bonding strength of soil skeleton is mainly provided by the solidification bond formed by a physicochemical interaction between particles. Its SYS is influenced by its cementation type, genetic characteristics, original rock structure, and environment. Because of the high clay content in Zhenlai saline soil, the specific surface area of soil particles is large, and the surface adsorption capacity of soil particles is strong. In addition, the main cation is Na+. The cementation strength of bound water film between soil particles is thus easily affected by water content and salt content, and compaction is also an important factor affecting the strength of soil. Therefore, in this study, the back-propagation neural network (BPNN) model and a support vector machine (SVM) are used to explore the relationship of saline soil’s SYS with its compactness, water content, and salt content. In total, 120 data points collected by a high-pressure consolidation experiment are applied to building BPNN and SVM model. For eliminate redundant features, Pearson correlation coefficient (rPCC) is used as an evaluation standard of feature selection. The K-fold cross-validation method was used to avoid over fitting. To compare the performance of the BPNN and SVM models, three statistical parameters were used: the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage deviation (MAPD). The result shows that the average values of R2, RMSE, and MAPD of the BPNN model are superior to the values of the SVM. We conclude that the BPNN model is slightly better than the SVM for predicting the SYS of saline soil. Thus, the BPNN model is used to analyze the factor sensitivity of SYS. The results indicate that the influence degrees of the three parameters are as follows: water content > compactness > salt content. This study can provide a basis for estimating the structural yield pressure of soil from its basic properties, and can provide a new way to obtain parameters for geotechnical engineering, ensuring safety while maintaining symmetry in engineering costs.

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Section: Model Performance Comparisonmentioning

confidence: 99%

Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

et al. 2020

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“…These limits, originally introduced by Atterberg [1,2] and later standardized for use in geoengineering applications by Terzaghi [3,4] and Casagrande [5,6], describe changes in the consistency states (and hence mechanical behavior) of fine-grained soils with respect to variations in water content. The LL and PL, together with their arithmetic difference, the plasticity index (PI), have been successfully incorporated into the soil mechanics framework, serving a variety of useful purposes, including their adoption for routine soil classification purposes [7][8][9][10], as well as their widespread applications for predicting useful soil properties (e.g., compactability, permeability, compressibility, and shear strength) for performing preliminary geotechnical designs [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Both the LL and PL tests are conventionally performed on the soil fraction passing the 425-µm sieve size.…”

Section: Introductionmentioning

confidence: 99%

Reappraisal of the ASTM/AASHTO Standard Rolling Device Method for Plastic Limit Determination of Fine-Grained Soils

Soltani

O’Kelly

2021

Geosciences

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Given its apparent limitations, various attempts have been made to develop alternative testing approaches to the standardized rolling-thread plastic limit (PLRT) method (for fine-grained soils), targeting higher degrees of repeatability and reproducibility. Among these, device-rolling techniques, including the method described in ASTM D4318/AASHTO T90 standards, based on original work by Bobrowski and Griekspoor (BG) and which follows the same basic principles as the standard thread-rolling (by hand) test, have been highly underrated by some researchers. To better understand the true potentials and/or limitations of the BG method for soil plasticity determination (i.e., PLBG), this paper presents a critical reappraisal of the PLRT–PLBG relationship using a comprehensive statistical analysis performed on a large and diverse database of 60 PLRT–PLBG test pairs. It is demonstrated that for a given fine-grained soil, the BG and RT methods produce essentially similar PL values. The 95% lower and upper (water content) statistical agreement limits between PLBG and PLRT were, respectively, obtained as −5.03% and +4.51%, and both deemed “statistically insignificant” when compared to the inductively-defined reference limit of ±8% (i.e., the highest possible difference in PLRT based on its repeatability, as reported in the literature). Furthermore, the likelihoods of PLBG underestimating and overestimating PLRT were 50% and 40%, respectively; debunking the notion presented by some researchers that the BG method generally tends to greatly underestimate PLRT. It is also shown that the degree of underestimation/overestimation does not systematically change with changes in basic soil properties; suggesting that the differences between PLBG and PLRT are most likely random in nature. Compared to PLRT, the likelihood of achieving consistent soil classifications employing PLBG (along with the liquid limit) was shown to be 98%, with the identified discrepancies being cases that plot relatively close to the A-Line. As such, PLBG can be used with confidence for soil classification purposes.

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A Two-Fold Empirical Approach for Estimating the Preconsolidation Stress in Clay Deposits

Kootahi

Mayne

2018

Proceedings of GeoShanghai 2018 International Conference: Multi-Physics Processes in Soil Mechanics and Advances in Geotechnica

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Index Test Method for Estimating the Effective Preconsolidation Stress in Clay Deposits

Cited by 14 publications

References 19 publications

Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

Reappraisal of the ASTM/AASHTO Standard Rolling Device Method for Plastic Limit Determination of Fine-Grained Soils

A Two-Fold Empirical Approach for Estimating the Preconsolidation Stress in Clay Deposits

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