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Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field t...

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Autores principales: Kim, Kyeongjin, Kim, WooSeok, Seo, Junwon, Jeong, Yoseok, Lee, Meeju, Lee, Jaeha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840728/
https://www.ncbi.nlm.nih.gov/pubmed/35160990
http://dx.doi.org/10.3390/ma15031045
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author Kim, Kyeongjin
Kim, WooSeok
Seo, Junwon
Jeong, Yoseok
Lee, Meeju
Lee, Jaeha
author_facet Kim, Kyeongjin
Kim, WooSeok
Seo, Junwon
Jeong, Yoseok
Lee, Meeju
Lee, Jaeha
author_sort Kim, Kyeongjin
collection PubMed
description In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field test. The numerical model was developed using smoothed particle hydrodynamics (SPH). SPH is a mesh-free method that can be used to predict the amount of fragments and their travel distances from concrete structures under impact loading. In addition, deep neural network (DNN) and gradient boosting machine (GBM) were also employed as machine learning methods. In this study, the results of DNN, GBM, and numerical analysis were then compared with the conducted field test. Such comparisons revealed that numerical analysis generated lower error than both DNN and GBM. When prediction results of both the amount of fragments and their travel distances were considered, the result of DNN showed smaller errors than that of GBM. Therefore, in studies where machine learning is used to predict the amount of fragments and their travel distances, careful selection of an appropriate method from the various available machine learning methods such as DNN, GBM, and random forest is absolutely important.
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spelling pubmed-88407282022-02-13 Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method Kim, Kyeongjin Kim, WooSeok Seo, Junwon Jeong, Yoseok Lee, Meeju Lee, Jaeha Materials (Basel) Article In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field test. The numerical model was developed using smoothed particle hydrodynamics (SPH). SPH is a mesh-free method that can be used to predict the amount of fragments and their travel distances from concrete structures under impact loading. In addition, deep neural network (DNN) and gradient boosting machine (GBM) were also employed as machine learning methods. In this study, the results of DNN, GBM, and numerical analysis were then compared with the conducted field test. Such comparisons revealed that numerical analysis generated lower error than both DNN and GBM. When prediction results of both the amount of fragments and their travel distances were considered, the result of DNN showed smaller errors than that of GBM. Therefore, in studies where machine learning is used to predict the amount of fragments and their travel distances, careful selection of an appropriate method from the various available machine learning methods such as DNN, GBM, and random forest is absolutely important. MDPI 2022-01-28 /pmc/articles/PMC8840728/ /pubmed/35160990 http://dx.doi.org/10.3390/ma15031045 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Kyeongjin
Kim, WooSeok
Seo, Junwon
Jeong, Yoseok
Lee, Meeju
Lee, Jaeha
Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method
title Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method
title_full Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method
title_fullStr Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method
title_full_unstemmed Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method
title_short Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method
title_sort prediction of concrete fragments amount and travel distance under impact loading using deep neural network and gradient boosting method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840728/
https://www.ncbi.nlm.nih.gov/pubmed/35160990
http://dx.doi.org/10.3390/ma15031045
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