Lateral deflection of piles in a multilayer soil medium. Case study: The Terengganu seaside platform

Moayedi, Hossein; Назир, Рамли; Mosallanezhad, Mansour; Noor, Rozaimi Bin Mohd; Khalilpour, Mehrdad

doi:10.1016/j.measurement.2018.03.068

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The non-linear rules between each set of inputs and their specific target(s) can be easily revealed using ANN [42]. Many scholars have used different types of ANN to model various engineering models [14,16,20,38,43,44]. Despite the explicit benefits of ANNs, the most significant disadvantages of them lie in being trapped in their local minima and overfitting.…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

“…where x and x j stand for the input vector and input pattern related to the jth instance, respectively. The vector w and the biasing threshold b are obtained from Equations (13) and (14) [60]:…”

Section: Improved Support Vector Machine Using Sequential Minimal Optmentioning

confidence: 99%

“…Also, ANN can be implemented using any determined number of hidden nodes [12,13]. For example, the suitable efficiency of an artificial neural network (ANN) [14][15][16] and a support vector machine (SVM) [17] has been proven in many geotechnical studies. After reviewing the literature, the complexity of the slope stability problem is evident.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

et al. 2019

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In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.

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Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Section: Improved Support Vector Machine Using Sequential Minimal Optmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

et al. 2019

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“…erefore, the bearing capacity of the soil has a great influence on the pile foundation, especially the vibration that is generated by an earthquake [21]. When an earthquake occurs, soft soils may be subjected to disfiguration [22], which can reduce the serviceability of the structure; meanwhile, the pile displacement accumulation [23] may result in damage to existing piles [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics of the Surrounding Soil during the Vibrational Pulling Process of a Pile Based on DEM

Sun

Zhang

Yang

et al. 2020

Shock and Vibration

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When bridge construction has been completed, the temporary support system, such as a steel-pipe pile, is dismantled, and problems that are related to the steel-pipe piles pulling from the strata are encountered. It is difficult to measure the dynamic characteristics of the surroundings during the deep pile-pulling process. A numerical model of the pulling pile is established by using the discrete element method (DEM). A sinusoidal dynamic load is executed on the pile with various vibrational frequencies. The stress, bond, coordination number, porosity, and velocity field distribution of the surrounding soil during the pile-pulling process are studied. The results demonstrate that, during the pile-pulling process, the shearing state of the surrounding soil depends on the position of the pile. The number and area of bond fractures gradually increase, the coordination number decreases, and the porosity gradually increases when the pile-pulling distance increases. Under a vibrational load, the area that corresponds to particles with large velocity and displacement expands with the increase of the vibrational frequency. The major influence zone of the pile pulling on both sides is concentrated within 6dpile, which can be used to evaluate the safety of the existing bridge nearby during pile pulling.

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“…Sci. 2019, 9, Few more studies have been performed on longitude strain, which have measured the instrumented piles [8][9][10][11][12][13], transferring of load in rapid pile axial loading [14], static, dynamic, seismic, and cyclic lateral load of pile classifications [15][16][17], rigid and flexible pile behaviors in diverse soft soils [18][19][20], and skin friction resistance measurement in piles [21]. Some studies have been conducted based on numerical simulations, however, others have been performed on field monitoring (Ng and Sritharan [22]; Hung et al [23]; Tafreshi et al [24]; Mascarucci et al [25]; and Lee et al [26]).…”

Section: Introductionmentioning

confidence: 99%

Determination of Young Elasticity Modulus in Bored Piles Through the Global Strain Extensometer Sensors and Real-Time Monitoring Data

et al. 2019

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For friction piles depending on the friction resistance, accurate prediction of unit skin friction around the pile shaft is the dominant resistance to measure the final bearing capacity of a bored-pile. The present study measures the stress-strain transferring in two instrumented bored-piles (BP #1 & BP# 2) embedded within the soil layer in Kuala Lumpur by real-time monitoring global strain extensometer (GSE) sensors. Two bored-piles (i.e., having 1.80 m and 1.0 m diameters, as well as 36 m and 32 m lengths) have been loaded with two times to their design working loads. Extensive data are analyzed to measure the changes in stress-strain in the bored-pile. The effect of loading and unloading stages on the pile's head and base settlement has been monitored, indicating that Young modulus of elasticity in concrete bored-pile (E c ), average strain, and unit skin friction changed along the bored-pile based on the ground site conditions and stress registered. One example of two case studies with great real-time monitoring data has been provided to further design.

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Lateral deflection of piles in a multilayer soil medium. Case study: The Terengganu seaside platform

Cited by 9 publications

References 8 publications

Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

Dynamic Characteristics of the Surrounding Soil during the Vibrational Pulling Process of a Pile Based on DEM

Determination of Young Elasticity Modulus in Bored Piles Through the Global Strain Extensometer Sensors and Real-Time Monitoring Data

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