Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohammed Amin Benbouras, Alexandru-Ionuţ Petrişor, Hamma Zedira, Laala Ghelani, Lina Lefilef
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pile-bearing capacity machine learning deep neural network <i>K</i>-fold cross-validation approach sensitivity analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/87b2eb427c144d3aaf2d2b429c5d7c07
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:87b2eb427c144d3aaf2d2b429c5d7c07
record_format	dspace
spelling	oai:doaj.org-article:87b2eb427c144d3aaf2d2b429c5d7c072021-11-25T16:40:34ZForecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques10.3390/app1122109082076-3417https://doaj.org/article/87b2eb427c144d3aaf2d2b429c5d7c072021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10908https://doaj.org/toc/2076-3417Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a <i>K</i>-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.Mohammed Amin BenbourasAlexandru-Ionuţ PetrişorHamma ZediraLaala GhelaniLina LefilefMDPI AGarticlepile-bearing capacitymachine learningdeep neural network<i>K</i>-fold cross-validation approachsensitivity analysisTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10908, p 10908 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pile-bearing capacity machine learning deep neural network <i>K</i>-fold cross-validation approach sensitivity analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	pile-bearing capacity machine learning deep neural network <i>K</i>-fold cross-validation approach sensitivity analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Mohammed Amin Benbouras Alexandru-Ionuţ Petrişor Hamma Zedira Laala Ghelani Lina Lefilef Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
description	Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a <i>K</i>-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.
format	article
author	Mohammed Amin Benbouras Alexandru-Ionuţ Petrişor Hamma Zedira Laala Ghelani Lina Lefilef
author_facet	Mohammed Amin Benbouras Alexandru-Ionuţ Petrişor Hamma Zedira Laala Ghelani Lina Lefilef
author_sort	Mohammed Amin Benbouras
title	Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
title_short	Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
title_full	Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
title_fullStr	Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
title_full_unstemmed	Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
title_sort	forecasting the bearing capacity of the driven piles using advanced machine-learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/87b2eb427c144d3aaf2d2b429c5d7c07
work_keys_str_mv	AT mohammedaminbenbouras forecastingthebearingcapacityofthedrivenpilesusingadvancedmachinelearningtechniques AT alexandruionutpetrisor forecastingthebearingcapacityofthedrivenpilesusingadvancedmachinelearningtechniques AT hammazedira forecastingthebearingcapacityofthedrivenpilesusingadvancedmachinelearningtechniques AT laalaghelani forecastingthebearingcapacityofthedrivenpilesusingadvancedmachinelearningtechniques AT linalefilef forecastingthebearingcapacityofthedrivenpilesusingadvancedmachinelearningtechniques
_version_	1718413088037797888

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

Ejemplares similares