Assessment of deflection of pile implanted on slope by artificial neural network

Goudjil, Kamel; Arabet, Leila

doi:10.1007/s00521-020-04985-6

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…The prediction results of two models show that the method of predicting the bearing capacity of the composite foundation of the vibrating gravel pile, based on a RBF neural network, is more accurate than that based on a BP neural network, and it takes less time to compute. KAL Goudjil [22] proposed an artificial neural network to predict the pile deflection, compared with a numerical modeling of an experimental model, using the three-dimensional finite element method. The results obtained are very satisfactory with very acceptable errors.…”

Section: Introductionmentioning

confidence: 99%

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Wang

Zhang

et al. 2023

Buildings

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The testing of the foundation pile is an important means to ensure the quality of the foundation pile in the construction process, and the low-strain pile test is one of the most commonly used testing technologies. However, in order to ensure that the testing signal is effective and reliable, it is necessary to provide the preliminary judgment results when acquiring the testing signal in the field. In this paper, we propose a data classification method for low-strain pile testing data using a recurrent neural network as the core. In this method, after identification, tailoring, and normalization, the input feature vector with a sequential structure is sent into this model. The model ensures the efficient use of data values while considering the sequential relationship among the data. At last, we designed and produced one complete model pile and six asymmetric model piles, which can form thirteen kinds of testing signals. The optimal application model was selected by the 10-fold cross verification method, and the influence of increasing the input feature dimension on the accuracy was discussed. Finally, compared with the other two methods, this model has the highest accuracy, at 98.46%, but it requires more training parameters and a longer training time.

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Section: Introductionmentioning

confidence: 99%

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Wang

Zhang

et al. 2023

Buildings

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“…ANNs are used less in geotechnical engineering than in other domains even though there is success in solving such problems (e.g. prediction of pile deflection [19], bearing capacity of foundations [20], seismic deformation of rooted slopes [21], etc. ).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks

Arabet

Hidjeb

Belaabed

2022

Eng. Technol. Appl. Sci. Res.

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For the prediction of the shear strength of reinforced soil many approaches are utilized which are complex and they depend on laboratory tests and several parameters. In this study, we aim to investigate and compare the ability of the Gray and Ohashi (GO) model and Artificial Neural Networks (ANNs) to predict the shear strength of reinforced soil. To achieve this objective, this work was divided into two parts. In the first part and in order to evaluate the impact of different fiber reinforcing parameters on the behavior of the soil, many direct shear experiments were carried out. The results revealed a significant improvement in shear strength values with fiber reinforcement. The increase in shear strength is a function of the fiber length, proportion, and direction. In the second part, we used the results of our experimental study to develop the ANN model. The obtained results agree reasonably well with the experiment ones, with very acceptable error (RMSE =1.714, MAE=5.981, R2= 0.960, and E = -1.601%). The comparative study showed that the ANN model was more accurate and statistically more stable than the GO model, and the ANN model took all the conditions of the reinforced soil into one equation. On the other hand, the GO model does not take reinforcement failure and uses several equations.

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Analysis and Stabilization of Toe Excavated Slopes Using Rigid Columns

Pomson,

Hari

2024

Lecture Notes in Civil Engineering

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Assessment of deflection of pile implanted on slope by artificial neural network

Cited by 9 publications

References 27 publications

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks

Analysis and Stabilization of Toe Excavated Slopes Using Rigid Columns

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