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Developing random forest hybridization models for estimating the axial bearing capacity of pile
Accurate determination of the axial load capacity of the pile is of utmost importance when designing the pile foundation. However, the methods of determining the axial load capacity of the pile in the field are often costly and time-consuming. Therefore, the purpose of this study is to develop a hyb...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936477/ https://www.ncbi.nlm.nih.gov/pubmed/35312706 http://dx.doi.org/10.1371/journal.pone.0265747 |
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author | Pham, Tuan Anh Tran, Van Quan |
author_facet | Pham, Tuan Anh Tran, Van Quan |
author_sort | Pham, Tuan Anh |
collection | PubMed |
description | Accurate determination of the axial load capacity of the pile is of utmost importance when designing the pile foundation. However, the methods of determining the axial load capacity of the pile in the field are often costly and time-consuming. Therefore, the purpose of this study is to develop a hybrid machine-learning to predict the axial load capacity of the pile. In particular, two powerful optimization algorithms named Herd Optimization (PSO) and Genetic Algorithm (GA) were used to evolve the Random Forest (RF) model architecture. For the research, the data set including 472 results of pile load tests in Ha Nam province—Vietnam was used to build and test the machine-learning models. The data set was divided into training and testing parts with ratio of 80% and 20%, respectively. Various performance indicators, namely absolute mean error (MAE), mean square root error (RMSE), and coefficient of determination (R(2)) are used to evaluate the performance of RF models. The results showed that, between the two optimization algorithms, GA gave superior performance compared to PSO in finding the best RF model architecture. In addition, the RF-GA model is also compared with the default RF model, the results show that the RF-GA model gives the best performance, with the balance on training and testing set, meaning avoiding the phenomenon of overfitting. The results of the study suggest a potential direction in the development of machine learning models in engineering in general and geotechnical engineering in particular. |
format | Online Article Text |
id | pubmed-8936477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89364772022-03-22 Developing random forest hybridization models for estimating the axial bearing capacity of pile Pham, Tuan Anh Tran, Van Quan PLoS One Research Article Accurate determination of the axial load capacity of the pile is of utmost importance when designing the pile foundation. However, the methods of determining the axial load capacity of the pile in the field are often costly and time-consuming. Therefore, the purpose of this study is to develop a hybrid machine-learning to predict the axial load capacity of the pile. In particular, two powerful optimization algorithms named Herd Optimization (PSO) and Genetic Algorithm (GA) were used to evolve the Random Forest (RF) model architecture. For the research, the data set including 472 results of pile load tests in Ha Nam province—Vietnam was used to build and test the machine-learning models. The data set was divided into training and testing parts with ratio of 80% and 20%, respectively. Various performance indicators, namely absolute mean error (MAE), mean square root error (RMSE), and coefficient of determination (R(2)) are used to evaluate the performance of RF models. The results showed that, between the two optimization algorithms, GA gave superior performance compared to PSO in finding the best RF model architecture. In addition, the RF-GA model is also compared with the default RF model, the results show that the RF-GA model gives the best performance, with the balance on training and testing set, meaning avoiding the phenomenon of overfitting. The results of the study suggest a potential direction in the development of machine learning models in engineering in general and geotechnical engineering in particular. Public Library of Science 2022-03-21 /pmc/articles/PMC8936477/ /pubmed/35312706 http://dx.doi.org/10.1371/journal.pone.0265747 Text en © 2022 Pham, Tran https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pham, Tuan Anh Tran, Van Quan Developing random forest hybridization models for estimating the axial bearing capacity of pile |
title | Developing random forest hybridization models for estimating the axial bearing capacity of pile |
title_full | Developing random forest hybridization models for estimating the axial bearing capacity of pile |
title_fullStr | Developing random forest hybridization models for estimating the axial bearing capacity of pile |
title_full_unstemmed | Developing random forest hybridization models for estimating the axial bearing capacity of pile |
title_short | Developing random forest hybridization models for estimating the axial bearing capacity of pile |
title_sort | developing random forest hybridization models for estimating the axial bearing capacity of pile |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936477/ https://www.ncbi.nlm.nih.gov/pubmed/35312706 http://dx.doi.org/10.1371/journal.pone.0265747 |
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