Prediction of the Axial Bearing Capacity of Piles by SPT-based and Numerical Design Methods

Shooshpasha, Issa; Hasanzadeh, Ali; Taghavi, Abbasali

doi:10.21660/2013.8.2118

Cited by 19 publications

(20 citation statements)

References 3 publications

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“…The static pile bearing capacity test (Igoe et al 2010;Adejumo 2013) is an expensive and time consuming procedure when compared with the dynamic method (O'Neill et al 1990) or the axial compressive force pulse test (Shooshpasha et al 2013). To perform the static test, one should install a costly axial load anchoring system of the tested pile or bring the ballast with mass or total force of reaction system larger than bearing capacity of the pile.…”

Section: Discussionmentioning

confidence: 99%

Study of Bearing Capacity of Vibratory Pile Applying Acceleration Record

Kelevišius

Gabrielaitis

Amšiejus

et al. 2014

Journal of Civil Engineering and Management

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The article focuses on the method for evaluation of ultimate bearing capacity for a vibratory pile having acceleration data recorded during the tests. The simulation vibratory pile installation test was performed in the testing stand. Accelerations were recorded on the top of the simulation vibratory pile during the test. The static test was performed for the installed pile. After the review of rheological models of the base, the Smith rheological model was chosen for determination of bearing capacity of the vibratory pile as this model, the rigidity of the final element of the spring is modelled as the finite rigidity of the base. Between the base of the modelled pile and the soil, a finite interface element is used. The interface element transfers only compression but it does not transfer tension to the base rheological model. The general stiffness of spring's finite element in the chosen rheological model is determined from experimental data of the static pile test. During the modelling, the damping coefficients and the ultimate displacements (responses) of the pile's shaft and base, to which the friction element became active, were determined so that the modelled pile accelerations and displacement (response) would coincide as much as possible with measured accelerations and their calculated response. The modelled and measured accelerations and responses showed high similarity.

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Section: Discussionmentioning

confidence: 99%

Study of Bearing Capacity of Vibratory Pile Applying Acceleration Record

Kelevišius

Gabrielaitis

Amšiejus

et al. 2014

Journal of Civil Engineering and Management

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“…In addition, many traditional formulas had been proposed, which were mainly based on the in situ testing result of soil such as cone penetration test (CPT) and standard penetration test (SPT) [5][6][7][8][9]. In some other studies, the authors also used the finite element method to analyze the pile load and pile displacement relationship of [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, for the pile load test method, although the percentage of reliability is high, it is time-consuming and costly and the equipment is often very cumbersome [15]. e dynamic method is highly dependent on the characteristics of the pile, the hammer, and the position of the pile to predict the bearing capacity of the pile without considering soil effects [11,16]. Last but not least, the characteristics of the numerical simulation method are finite-element-based; basically still approximate method and the results depend a lot on the modeling process [17].…”

Section: Introductionmentioning

confidence: 99%

Application of Ensemble Learning Using Weight Voting Protocol in the Prediction of Pile Bearing Capacity

Pham

2021

Mathematical Problems in Engineering

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Accurate prediction of pile bearing capacity is an important part of foundation engineering. Notably, the determination of pile bearing capacity through an in situ load test is costly and time-consuming. Therefore, this study focused on developing a machine learning algorithm, namely, Ensemble Learning (EL), using weight voting protocol of three base machine learning algorithms, gradient boosting (GB), random forest (RF), and classic linear regression (LR), to predict the bearing capacity of the pile. Data includes 108 pile load tests under different conditions used for model training and testing. Performance evaluation indicators such as R-square (R2), root mean square error (RMSE), and MAE (mean absolute error) were used to evaluate the performance of models showing the efficiency of predicting pile bearing capacity with outstanding performance compared to other models. The results also showed that the EL model with a weight combination of w 1 = 0.482, w 2 = 0.338, and w 3 = 0.18 corresponding to the models GB, RF, and LR gave the best performance and achieved the best balance on all data sets. In addition, the global sensitivity analysis technique was used to detect the most important input features in determining the bearing capacity of the pile. This study provides an effective tool to predict pile load capacity with expert performance.

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“…In pile foundation design, the axial pile bearing capacity (P u ) is considered one of the most critical parameters [ 1 ]. Throughout years of research and development, five main approaches to determine the pile bearing capacity have been adopted, namely the static analysis, dynamic analysis, dynamic testing, pile load testing, and in-situ testing [ 2 ]. It is needless to say each of the above methods possesses advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity

Pham

Tran

et al. 2020

PLoS ONE

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Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this purpose, a Genetic Algorithm (GA) was developed to select the most significant features in the raw dataset. After that, a GA-DLNN hybrid model was developed to select optimal parameters for the DLNN model, including: network algorithm, activation function for hidden neurons, number of hidden layers, and the number of neurons in each hidden layer. A database containing 472 driven pile static load test reports was used. The dataset was divided into three parts, namely the training set (60%), validation (20%) and testing set (20%) for the construction, validation and testing phases of the proposed model, respectively. Various quality assessment criteria, namely the coefficient of determination (R2), Index of Agreement (IA), mean absolute error (MAE) and root mean squared error (RMSE), were used to evaluate the performance of the machine learning (ML) algorithms. The GA-DLNN hybrid model was shown to exhibit the ability to find the most optimal set of parameters for the prediction process.The results showed that the performance of the hybrid model using only the most critical features gave the highest accuracy, compared with those obtained by the hybrid model using all input variables.

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Prediction of the Axial Bearing Capacity of Piles by SPT-based and Numerical Design Methods

Cited by 19 publications

References 3 publications

Study of Bearing Capacity of Vibratory Pile Applying Acceleration Record

Study of Bearing Capacity of Vibratory Pile Applying Acceleration Record

Application of Ensemble Learning Using Weight Voting Protocol in the Prediction of Pile Bearing Capacity

Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity

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