Shallow Sliding Failure Prediction Model of Expansive Soil Slope based on Gaussian Process Theory and Its Engineering Application

He, Peng; Li, Shucai; Xiao, Jie; Zhang, Qian‐qing; Xu, Fei; Zhang, Jian

doi:10.1007/s12205-017-1934-6

Cited by 25 publications

(12 citation statements)

References 14 publications

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“…The notation S NL ðσÞ = S NL ðσjA, n, TÞ emphasizes the dependence of strength functions on their parameters. Particularly, A is the scale parameter controlling the magnitude of shear strength, T is a shifting parameter controlling the location of the envelope on the σ axis (t NL = P a T is the tensile strength associated with S NL ðσÞ = S NL ðσjA, n, TÞ, with T representing a nondimensional tensile strength), and n controls the curvature of the envelope [7]. Here, A > 0, 1/2 ≤ n ≤ 1, T ≥ 0.…”

Section: Shallow Layer Failure Model Of Expansive Soil Slopementioning

confidence: 99%

“…Almost all expansive soil slope failures are in the form of shallow layer slipping failure, and the overburden pressure of the soil above the slip surface is less than the value specified in the conventional shear test. The dry-wet cyclic shear test with loading has been carried out on undisturbed soil [7,31,32], revealing the effects of dry-wet cycles, especially of low stress, on the nonlinear shear strength of expansive soil. The test results are presented in Table 1.…”

Section: Test Analysis Of the Nonlinear Shearmentioning

confidence: 99%

“…(Since the point of this work is the shallow layer destruction law of expansive soil slope under rainfall and application of geogrid reinforcement, here is a brief introduction of the shear strength of expansive soil under wet-dry cycles with loading. To make it easier to understand, some related studies are available for References [7,31,32]. )…”

Section: Test Analysis Of the Nonlinear Shearmentioning

confidence: 99%

“…When the thickness of the sliding body is less than 1.5 m, the vertical pressure of the sliding soil becomes less than 30 kPa. As the measured shear strength of the soil is often nonlinear [6][7][8], the strength parameters that match actual engineering conditions can be obtained only when conducting the shear test under low stress.…”

Section: Introductionmentioning

confidence: 99%

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Shallow Layer Destruction Law of Expansive Soil Slope under Rainfall and the Application of Geogrid Reinforcement

Chen

Wang

et al. 2021

Geofluids

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A large number of instability cases and laboratory tests of expansive soil slopes show that its shallow layer destruction happens because of the insufficient shear strength under the usual action of low stress and repeated dry-wet cycles. We can obtain the strength nonlinear distribution law fitted by generalized power function based on a series of shear strength tests of expansive soil considering low stress and can construct the numerical model considering the nonlinear strength distribution by FISH, to realize the shear strength dynamic distribution with the vertical stress. Based on the numerical model, the whole-process contrastive analysis has been conducted on the stress field, the slip surface depth, and the seepage field of plain soil and reinforced expansive soil cut slope under different rainfall conditions. Besides, the mechanics characteristic of the geogrid under various design schemes has been compared and analyzed. A further explanation has been given for the expansive soil cut slope prone to shallow layer failure after rainfall and on the effect of geogrid reinforcement. The numerical results provide a reference for slope stability analysis in rainy expansive soil areas.

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Section: Shallow Layer Failure Model Of Expansive Soil Slopementioning

confidence: 99%

Section: Test Analysis Of the Nonlinear Shearmentioning

confidence: 99%

Section: Test Analysis Of the Nonlinear Shearmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shallow Layer Destruction Law of Expansive Soil Slope under Rainfall and the Application of Geogrid Reinforcement

Chen

Wang

et al. 2021

Geofluids

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“…The developed daily electricity consumption prediction model is validated through performance comparisons against a set of four widelyrecognized machine learning models namely, radial basis neural network ( ), support vector machines ( ), decision tree ( ) and Gaussian process ( ). More information about , , , and can be adopted from Gholami et al [21], He et al [22], El-Zahab et al [23] and Kutyłowska [24]. The performance comparisons are carried out using split validation based on mean absolute error ( ), mean absolute percentage error ( ) and rootmean squared error (…”

Section: Model Developmentmentioning

confidence: 99%

On the Implementation of Machine Learning Models for Emulating Daily Electricity Consumption in Hotel Facilities

Abdelkader¹,

Al-Sakkaf²,

Elshaboury³

et al. 2021

World Congress on Civil, Structural, and Environmental Engineering

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The increase in electricity consumption has negative effects on the environment, including greenhouse gas emissions, global warming, and rapid climate change. In an attempt to address this global problem, this research aims at developing machine learning models for the prediction of electricity consumption in hotels. The models are implemented using several techniques, including K-nearest neighbors, radial basis neural network, support vector machines, decision tree, and Gaussian process. The performance of the aforementioned models is evaluated by measuring the mean absolute error, mean absolute percentage error and root-mean squared error using split validation. Finally, two-tailed Student's t-tests are performed to evaluate the statistical significance level of prediction models. The results demonstrate that the K-nearest neighbors model accomplished the highest prediction performance yielding mean absolute error, mean absolute percentage error and root-mean squared error of 130.77, 276.45 and 4.79%, respectively. Support vector machines provided the lowest prediction accuracy accomplishing mean absolute error, mean absolute percentage error and root-mean squared error of 303.39, 382.78 and 11.98%, respectively.

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GPR‐MCS model of reliability analysis of key blocks and its engineering application

Wang

et al. 2021

Num Anal Meth Geomechanics

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Reasonable rock bolting schemes for potentially unstable blocks should be proposed and optimized in order to prevent rockfall hazard during tunnel construction. However, the reliability index of key block caused by uncertainty of geometric and mechanics parameters is not easy to be obtained by numerical simulation. Since the performance function of stability analysis of supporting key block is too complex, it is also difficult to calculate the reliability by using analytical and graphic methods rapidly. In this paper, the Gaussian process regression (GPR) model was established instead of the explicit function of key block. The safety factor of supporting key block calculated by UnWedge procedure was taken as the sample output of response model. With the GPR model coupling Monte Carlo Simulation (GPR‐MCS), the failure probability of key block could be predicted rapidly. The reliability of key block with different supporting schemes were analyzed, and the optimization of support parameters for key block was also carried out. It has direct meaning to prevention and control of dangerous key block during tunnel construction.

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Shallow Sliding Failure Prediction Model of Expansive Soil Slope based on Gaussian Process Theory and Its Engineering Application

Cited by 25 publications

References 14 publications

Shallow Layer Destruction Law of Expansive Soil Slope under Rainfall and the Application of Geogrid Reinforcement

Shallow Layer Destruction Law of Expansive Soil Slope under Rainfall and the Application of Geogrid Reinforcement

On the Implementation of Machine Learning Models for Emulating Daily Electricity Consumption in Hotel Facilities

GPR‐MCS model of reliability analysis of key blocks and its engineering application

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