Estimation of unsaturated shear strength from soil–water characteristic curve

Zhai, Qian; Rahardjo, Harianto; Satyanaga, Alfrendo; Dai, Guoliang

doi:10.1007/s11440-019-00785-y

Cited by 89 publications

(40 citation statements)

References 31 publications

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“…Many researchers have attempted to estimate the shear strength of soil using different alternative methods [3,[5][6][7][8][9][10]. The shear strength of unsaturated soil can also be predicted using the empirical correlation function [10,11], using soil-water retention curve [10,12,13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

Pham

et al. 2020

Sustainability

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Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of the models also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) is superior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, the proposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which can be used for the suitable designing of civil engineering structures.

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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

Pham

et al. 2020

Sustainability

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“…Conventional models (like multivariate approaches [13] and empirical methods [14]) have been widely used as SSS evaluative models. Also, in studies carried out by Zhai et al [15] and Al Aqtash and Bandini [16], a model for forecasting unsaturated shear strength was suggested based on the soil-water characteristic curve. Likewise, some evaluative models based on the soil-water retention curve (SWRC) have been explored by Garven and Vanapalli [17].…”

Section: Introductionmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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“…Before freezing (i.e., cooling process), although all specimens had identical global water saturation of S = 30%, the evaporation-driven specimens showed the highest P - and S -wave velocities and the horizontally-layered specimens had the lowest velocities ( Figure 8 a,b). The associated phenomena showed that the capillary force between particles built in the unsaturated condition contributed to increased effective stress; however, the capillarity varied with water distribution patterns [ 18 , 19 , 20 , 21 , 22 ]. The test specimens were prepared in the same manner except for the water distribution patterns.…”

Section: Resultsmentioning

confidence: 99%

Evolution of Small Strain Soil Stiffness during Freeze-Thaw Cycle: Transition from Capillarity to Cementation Examined Using Magnetic and Piezo Crystal Sensors

Park

Lee

Won

et al. 2021

Sensors

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Freeze-thaw cycles caused by seasonal temperature fluctuations significantly affect the geotechnical engineering properties. This study investigated the crucial role of water distribution patterns in the characterization of elastic wave properties for the fine F-110 sand during a freeze-thaw cycle. Sand specimens with four different water distribution patterns were prepared, namely homogeneously-mixed, evaporation-driven, vertically-, and horizontally-layered specimens. The P- and S-wave signatures of the specimens were monitored using piezo crystal sensors. Results indicated the criticality of water distribution patterns in the determination of small-strain soil properties even though the specimens had identical global water saturation. The nuclear magnetic resonance-based water volume depth profiles indicated that the evaporation-driven specimens had more heterogeneous pore-invasive ice-bonding layers at a high water saturation region; by contrast, the drying process facilitated uniform meniscuses around the particle contacts near the air percolation threshold. Elastic wave measurements for laboratory-prepared specimens might over/underestimate the small-strain soil stiffness of sediments in nature, wherein the drying processes prevailed to control the water saturation. This study highlighted a clear transition from capillary-controlled to cementation-controlled elastic wave properties during temperature oscillations.

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Estimation of unsaturated shear strength from soil–water characteristic curve

Cited by 89 publications

References 31 publications

A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Evolution of Small Strain Soil Stiffness during Freeze-Thaw Cycle: Transition from Capillarity to Cementation Examined Using Magnetic and Piezo Crystal Sensors

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