Prediction of friction capacity of driven piles in clay using artificial intelligence techniques

Suman, Shakti; Das, Sarat Kumar; Mohanty, Ranajeet

doi:10.1080/19386362.2016.1169009

Cited by 38 publications

(11 citation statements)

References 22 publications

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“…Samui (2008Samui ( , 2011 and Prayogo (2018) used a support vector machine (SVM) approach to predict the skin friction capacity of piles embedded in clay and also found that SVM provided good prediction of the skin friction capacity as the SVM produced low root mean square error values (which ranged between 4.4 to 13.9), low mean absolute error values (which ranged between 3.2 to 9.4), and high R values (which ranged between 0.93 to 0.99). Suman et al (2016) tested the capabilities of multivariate adaptive regression splines (MARS) and functional networks (FN) to predict the skin friction of driven piles embedded in clay. Suman et al (2016) found that these methods predicted the skin friction capacity with an accuracy better than the ANN, SVM, Alpha and Beta methods, as these methods scored lower mean absolute error values and lower root mean square error values.…”

Section: Introductionmentioning

confidence: 99%

“…Suman et al (2016) tested the capabilities of multivariate adaptive regression splines (MARS) and functional networks (FN) to predict the skin friction of driven piles embedded in clay. Suman et al (2016) found that these methods predicted the skin friction capacity with an accuracy better than the ANN, SVM, Alpha and Beta methods, as these methods scored lower mean absolute error values and lower root mean square error values. Moayedi and Hayati (2018b) developed design charts and a mathematical model to predict the skin friction capacity of driven piles embedded in clay.…”

Section: Introductionmentioning

confidence: 99%

“…Also, no study in the literature has evaluated the capabilities of multi-objective evolutionary polynomial regression analysis (MOGA-EPR) in predicting the skin friction capacity of piles, although this method provides robust and simple mathematical models as demonstrated in many studies in the literature (Ahangar-Asr et al, 2014, 2016Fiore et al, 2016;Alzabeebee et al, 2018Alzabeebee et al, , 2019Alzabeebee, 2019Alzabeebee, , 2020). In addition, previous studies evaluated the accuracy of Alpha method in predicting the skin friction capacity of bored piles (Cherubini and Vessia, 2007), and Alpha and Beta methods for the case of driven piles (Goh, 1995;Suman et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods

Alzabeebee

Chapman

2020

Transportation Geotechnics

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Link to publication on Research at Birmingham portal General rightsUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.• Users may freely distribute the URL that is used to identify this publication.• Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.• User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain.Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.When citing, please reference the published version. Take down policyWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods

Alzabeebee

Chapman

2020

Transportation Geotechnics

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“…On the other hand, the base geometry is as inverted to the cone. Accordingly, different computational models have been used to analyze the pile behavior in various independent loadings, lateral loadings, vertical-uplift, and vertical compressive [1,2,3,4,5], besides, the forecasting of the (1) bearing capacity of pile foundation [6,7]; (2) uplift capacity of suction caisson [8]; (3) pile dynamic capacity [9,10]; (4) pile setup [11]; and (5) pile settlements [12] has defined artificial neural network (ANN) to forecast the pullout capacity of suction foundations through the applying of a database, including the results of centrifuge tests. Moreover, Ardalan et al [13] have investigated GMDH (group method of data handling from neural networks’ family) with GA (genetic algorithms) indicating the effectual cone point resistance and cone sleeve friction on the inputs values of pile unit shaft resistance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques

Bui

Moayedi

Abdullahi

et al. 2019

Sensors

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The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.

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“…Efforts were limited to comparing f s prediction from various semiempirical methods with results from pile load tests [3][4][5]. To overcome this limitation, other efforts were dedicated to predicting f s through machine learning techniques [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

Prayogo

Susanto

2018

Advances in Civil Engineering

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is research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (ST-LSSVM), to accurately model the friction capacity of driven piles in cohesive soil.e hybrid approach uses LS-SVM as a supervised-learning-based predictor to build an accurate input-output relationship of the dataset and SOS method to optimize the σ and c parameters of the LS-SVM. Evaluation and investigation of the ST-LSSVM were conducted on 45 training data and 20 testing data of driven pile load tests that were compiled from previous studies. e prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). e comparison showed that the ST-LSSVM performed better than LS-SVM, BPNN, and NN in terms of R, RMSE, and MAE. is comprehensive evaluation confirmed the capability of hybrid approach SOS and LS-SVM to modeling the accurate friction capacity of driven piles in clay. It makes for a reliable and robust assistance tool in helping all geotechnical engineers estimate friction pile capacity.

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Prediction of friction capacity of driven piles in clay using artificial intelligence techniques

Cited by 38 publications

References 22 publications

Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods

Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods

Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

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