A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty
Owing to the increasing use of permeable pavement, there is a growing need for studies that can improve its design and durability. One of the most important factors that can reduce the functionality of permeable pavement is the clogging issue. Field experiments for investigating the clogging potenti...
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doaj-88870f2fca944672ab9d83db41b48f952021-06-01T00:46:36ZengMDPI AGSensors1424-82202021-05-01213603360310.3390/s21113603A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient PenaltyLudia Eka Feri0Jaehun Ahn1Shahrullohon Lutfillohonov2Joonho Kwon3Department of Big Data, Pusan National University, Busan 46241, KoreaDepartment of Civil and Environmental Engineering, Pusan National University, Busan 46241, KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, KoreaOwing to the increasing use of permeable pavement, there is a growing need for studies that can improve its design and durability. One of the most important factors that can reduce the functionality of permeable pavement is the clogging issue. Field experiments for investigating the clogging potential are relatively expensive owing to the high-cost testing equipment and materials. Moreover, a lot of time is required for conducting real physical experiments to obtain physical properties for permeable pavement. In this paper, to overcome these limitations, we propose a three-dimensional microstructure reconstruction framework based on 3D-IDWGAN with an enhanced gradient penalty, which is an image-based computational system for clogging analysis in permeable pavement. Our proposed system first takes a two-dimensional image as an input and extracts latent features from the 2D image. Then, it generates a 3D microstructure image through the generative adversarial network part of our model with the enhanced gradient penalty. For checking the effectiveness of our system, we utilize the reconstructed 3D image combined with the numerical method for pavement microstructure analysis. Our results show improvements in the three-dimensional image generation of the microstructure, compared with other generative adversarial network methods, and the values of physical properties extracted from our model are similar to those obtained via real pavement samples.https://www.mdpi.com/1424-8220/21/11/36033D microstructure reconstructionpermeable pavementdeep learninggenerative adversarial networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ludia Eka Feri Jaehun Ahn Shahrullohon Lutfillohonov Joonho Kwon |
spellingShingle |
Ludia Eka Feri Jaehun Ahn Shahrullohon Lutfillohonov Joonho Kwon A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty Sensors 3D microstructure reconstruction permeable pavement deep learning generative adversarial networks |
author_facet |
Ludia Eka Feri Jaehun Ahn Shahrullohon Lutfillohonov Joonho Kwon |
author_sort |
Ludia Eka Feri |
title |
A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty |
title_short |
A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty |
title_full |
A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty |
title_fullStr |
A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty |
title_full_unstemmed |
A Three-Dimensional Microstructure Reconstruction Framework for Permeable Pavement Analysis Based on 3D-IWGAN with Enhanced Gradient Penalty |
title_sort |
three-dimensional microstructure reconstruction framework for permeable pavement analysis based on 3d-iwgan with enhanced gradient penalty |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-05-01 |
description |
Owing to the increasing use of permeable pavement, there is a growing need for studies that can improve its design and durability. One of the most important factors that can reduce the functionality of permeable pavement is the clogging issue. Field experiments for investigating the clogging potential are relatively expensive owing to the high-cost testing equipment and materials. Moreover, a lot of time is required for conducting real physical experiments to obtain physical properties for permeable pavement. In this paper, to overcome these limitations, we propose a three-dimensional microstructure reconstruction framework based on 3D-IDWGAN with an enhanced gradient penalty, which is an image-based computational system for clogging analysis in permeable pavement. Our proposed system first takes a two-dimensional image as an input and extracts latent features from the 2D image. Then, it generates a 3D microstructure image through the generative adversarial network part of our model with the enhanced gradient penalty. For checking the effectiveness of our system, we utilize the reconstructed 3D image combined with the numerical method for pavement microstructure analysis. Our results show improvements in the three-dimensional image generation of the microstructure, compared with other generative adversarial network methods, and the values of physical properties extracted from our model are similar to those obtained via real pavement samples. |
topic |
3D microstructure reconstruction permeable pavement deep learning generative adversarial networks |
url |
https://www.mdpi.com/1424-8220/21/11/3603 |
work_keys_str_mv |
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