Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Jebur, Ameer A.; Atherton, William; Khaddar, Rafid M. Al; Loffill, Edward

doi:10.1080/19648189.2018.1531269

Cited by 16 publications

(6 citation statements)

References 44 publications

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“…. [7] [8]Used the Artificial Neural Network approach to develop a model to anticipate the results of a comprehensive Static Load Pile test. The model could anticipate the full pile load test from start to finish by incorporating the pile configuration, soil parameters, and groundwater table in a single artificial neural network model.…”

Section: Related Workmentioning

confidence: 99%

Prediction Load-Settlement of Bored PileS Using Artificial Neural Network

Jamel,

Aljanabi

2024

AJES

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Section: Related Workmentioning

confidence: 99%

Prediction Load-Settlement of Bored PileS Using Artificial Neural Network

Jamel,

Aljanabi

2024

AJES

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“…In the BPNN, the transfer functions from the input layer to the hidden layer and from the hidden layer to the output layer are the tanh function (Equation ( 14)) and the relu function (Equation ( 15)), respectively. As the training function of BPNN, the Levenberg-Marquardt (LM) backpropagation training algorithm [28,29], which has the fastest training speed and good fitting effect, is selected.…”

Section: Bpnnmentioning

confidence: 99%

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

Xi,

Jin,

et al. 2024

Mathematics

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The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects.

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“…In the field of the foundation pile test, many studies have applied neural networks to specific testing data analysis. Jebur et al [19] used a new artificial neural network (ANN) method to examine pile bearing capacity and to provide a reliable model to simulate pile load-settlement behavior. Alzo'ubi et al [20] proposed an artificial neural network approach to build a model that can predict a complete static load pile test.…”

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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Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Abstract: Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Cited by 16 publications

References 44 publications

Prediction Load-Settlement of Bored PileS Using Artificial Neural Network

Prediction Load-Settlement of Bored PileS Using Artificial Neural Network

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

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

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