Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network

To predict the damage resulting from an explosion in the middle of a city, where buildings are concentrated, the peak pressure reaching the walls of the buildings or in between buildings should be accurately and rapidly calculated. However, predicting peak pressure between buildings is known to be very difficult because of the diffraction and reflection of blast waves, which have generally been analyzed by numerical analysis methods. However, numerical analysis is not suitable in a military operation environment which requires rapid analysis, because it takes considerable time and resources. This study proposes a deep neural network that quickly and accurately predicts the peak pressure caused by the propagation of blast waves, for the effective analysis of weapon effectiveness and damage in urban environments. The proposed deep learning model is based on a 3-dimensional convolutional neural network (3D CNN) model that processes the spatial information of explosion and measurement in the 3D spaces using 3D kernels. To predict the peak pressure between buildings separated by an arbitrary distance using a single model, we also propose using conditional convolution, which modulates the prediction output according to the building distance. The proposed models were trained with a dataset constructed through finite element analysis with various building distances, explosion locations, and explosive weights. The experiment with a fixed building distance showed that the relative error of the proposed 3D CNN is less than 7%, which is 2.5 times more accurate than a simple multi-layer perceptron (MLP) model. For unseen building layouts, the conditional 3D convolution showed 3.6 times lower error than the MLP model, demonstrating the effectiveness of the conditional convolution for prediction in arbitrary building layouts. Most importantly, the proposed deep learning models took less than one minute per prediction, which is significantly faster than finite element analysis, which takes 6 to 8 hours to analyze a single simulation case. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Min Ah Kang [verfasserIn] Cheong Hee Park [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Blast wave propagation blast response damage assessment CNN deep learning CFD

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 26114-26124
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:26114-26124

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3257345

Katalog-ID:	DOAJ088908844

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callnumber-first	T - Technology
author	Min Ah Kang
spellingShingle	Min Ah Kang misc TK1-9971 misc Blast wave propagation misc blast response misc damage assessment misc CNN misc deep learning misc CFD misc Electrical engineering. Electronics. Nuclear engineering Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network
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topic_title	TK1-9971 Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network Blast wave propagation blast response damage assessment CNN deep learning CFD
topic	misc TK1-9971 misc Blast wave propagation misc blast response misc damage assessment misc CNN misc deep learning misc CFD misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Blast wave propagation misc blast response misc damage assessment misc CNN misc deep learning misc CFD misc Electrical engineering. Electronics. Nuclear engineering
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title	Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network
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title_full	Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network
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title_sort	prediction of peak pressure by blast wave propagation between buildings using a conditional 3d convolutional neural network
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title_auth	Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network
abstract	To predict the damage resulting from an explosion in the middle of a city, where buildings are concentrated, the peak pressure reaching the walls of the buildings or in between buildings should be accurately and rapidly calculated. However, predicting peak pressure between buildings is known to be very difficult because of the diffraction and reflection of blast waves, which have generally been analyzed by numerical analysis methods. However, numerical analysis is not suitable in a military operation environment which requires rapid analysis, because it takes considerable time and resources. This study proposes a deep neural network that quickly and accurately predicts the peak pressure caused by the propagation of blast waves, for the effective analysis of weapon effectiveness and damage in urban environments. The proposed deep learning model is based on a 3-dimensional convolutional neural network (3D CNN) model that processes the spatial information of explosion and measurement in the 3D spaces using 3D kernels. To predict the peak pressure between buildings separated by an arbitrary distance using a single model, we also propose using conditional convolution, which modulates the prediction output according to the building distance. The proposed models were trained with a dataset constructed through finite element analysis with various building distances, explosion locations, and explosive weights. The experiment with a fixed building distance showed that the relative error of the proposed 3D CNN is less than 7%, which is 2.5 times more accurate than a simple multi-layer perceptron (MLP) model. For unseen building layouts, the conditional 3D convolution showed 3.6 times lower error than the MLP model, demonstrating the effectiveness of the conditional convolution for prediction in arbitrary building layouts. Most importantly, the proposed deep learning models took less than one minute per prediction, which is significantly faster than finite element analysis, which takes 6 to 8 hours to analyze a single simulation case.
abstractGer	To predict the damage resulting from an explosion in the middle of a city, where buildings are concentrated, the peak pressure reaching the walls of the buildings or in between buildings should be accurately and rapidly calculated. However, predicting peak pressure between buildings is known to be very difficult because of the diffraction and reflection of blast waves, which have generally been analyzed by numerical analysis methods. However, numerical analysis is not suitable in a military operation environment which requires rapid analysis, because it takes considerable time and resources. This study proposes a deep neural network that quickly and accurately predicts the peak pressure caused by the propagation of blast waves, for the effective analysis of weapon effectiveness and damage in urban environments. The proposed deep learning model is based on a 3-dimensional convolutional neural network (3D CNN) model that processes the spatial information of explosion and measurement in the 3D spaces using 3D kernels. To predict the peak pressure between buildings separated by an arbitrary distance using a single model, we also propose using conditional convolution, which modulates the prediction output according to the building distance. The proposed models were trained with a dataset constructed through finite element analysis with various building distances, explosion locations, and explosive weights. The experiment with a fixed building distance showed that the relative error of the proposed 3D CNN is less than 7%, which is 2.5 times more accurate than a simple multi-layer perceptron (MLP) model. For unseen building layouts, the conditional 3D convolution showed 3.6 times lower error than the MLP model, demonstrating the effectiveness of the conditional convolution for prediction in arbitrary building layouts. Most importantly, the proposed deep learning models took less than one minute per prediction, which is significantly faster than finite element analysis, which takes 6 to 8 hours to analyze a single simulation case.
abstract_unstemmed	To predict the damage resulting from an explosion in the middle of a city, where buildings are concentrated, the peak pressure reaching the walls of the buildings or in between buildings should be accurately and rapidly calculated. However, predicting peak pressure between buildings is known to be very difficult because of the diffraction and reflection of blast waves, which have generally been analyzed by numerical analysis methods. However, numerical analysis is not suitable in a military operation environment which requires rapid analysis, because it takes considerable time and resources. This study proposes a deep neural network that quickly and accurately predicts the peak pressure caused by the propagation of blast waves, for the effective analysis of weapon effectiveness and damage in urban environments. The proposed deep learning model is based on a 3-dimensional convolutional neural network (3D CNN) model that processes the spatial information of explosion and measurement in the 3D spaces using 3D kernels. To predict the peak pressure between buildings separated by an arbitrary distance using a single model, we also propose using conditional convolution, which modulates the prediction output according to the building distance. The proposed models were trained with a dataset constructed through finite element analysis with various building distances, explosion locations, and explosive weights. The experiment with a fixed building distance showed that the relative error of the proposed 3D CNN is less than 7%, which is 2.5 times more accurate than a simple multi-layer perceptron (MLP) model. For unseen building layouts, the conditional 3D convolution showed 3.6 times lower error than the MLP model, demonstrating the effectiveness of the conditional convolution for prediction in arbitrary building layouts. Most importantly, the proposed deep learning models took less than one minute per prediction, which is significantly faster than finite element analysis, which takes 6 to 8 hours to analyze a single simulation case.
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title_short	Prediction of Peak Pressure by Blast Wave Propagation Between Buildings Using a Conditional 3D Convolutional Neural Network
url	https://doi.org/10.1109/ACCESS.2023.3257345 https://doaj.org/article/29d0dbdd0c46442092dd6fcda60f48f1 https://ieeexplore.ieee.org/document/10070763/ https://doaj.org/toc/2169-3536
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doi_str	10.1109/ACCESS.2023.3257345
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up_date	2024-07-03T20:11:09.336Z
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