Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method

The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns’ surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed...
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Autor*in:	Yutao Zhang [verfasserIn] Zijie He [verfasserIn] Ruonan Jiang [verfasserIn] Lei Liao [verfasserIn] Qingxiang Meng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Xiyu conglomerate machine learning digital image discrete element method mechanical properties

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 24, p 13000
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:24, p 13000

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app132413000

Katalog-ID:	DOAJ098919032

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spelling	10.3390/app132413000 doi (DE-627)DOAJ098919032 (DE-599)DOAJec34cce9623b43b9b1443e816d5bf7df DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Yutao Zhang verfasserin aut Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns’ surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed for the detection of mesoscale geomaterial structures. Computerized tomography (CT) scan images of the Xiyu conglomerate were employed to establish a high-precision numerical model. From the results of segmentation, the proposed algorithm outperformed UNet, HRNet, and the original SegFormer neural network. The segmentation results were used to calculate the porosity, and biaxial compression numerical simulation experiments based on the real structure were carried out using the particle flow code (PFC). We observed the failure process of the model and obtained the shear strength of the Xiyu conglomerate. We explored the causes and influencing factors of the anisotropy of the Xiyu conglomerate from the microstructure perspective and provide a micro-observation basis for establishing an anisotropic mechanical model. Xiyu conglomerate machine learning digital image discrete element method mechanical properties Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Zijie He verfasserin aut Ruonan Jiang verfasserin aut Lei Liao verfasserin aut Qingxiang Meng verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 24, p 13000 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:24, p 13000 https://doi.org/10.3390/app132413000 kostenfrei https://doaj.org/article/ec34cce9623b43b9b1443e816d5bf7df kostenfrei https://www.mdpi.com/2076-3417/13/24/13000 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 24, p 13000
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topic_facet	Xiyu conglomerate machine learning digital image discrete element method mechanical properties Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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container_title	Applied Sciences
authorswithroles_txt_mv	Yutao Zhang @@aut@@ Zijie He @@aut@@ Ruonan Jiang @@aut@@ Lei Liao @@aut@@ Qingxiang Meng @@aut@@
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callnumber-first	T - Technology
author	Yutao Zhang
spellingShingle	Yutao Zhang misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc Xiyu conglomerate misc machine learning misc digital image misc discrete element method misc mechanical properties misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method Xiyu conglomerate machine learning digital image discrete element method mechanical properties
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc Xiyu conglomerate misc machine learning misc digital image misc discrete element method misc mechanical properties misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_unstemmed	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc Xiyu conglomerate misc machine learning misc digital image misc discrete element method misc mechanical properties misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc Xiyu conglomerate misc machine learning misc digital image misc discrete element method misc mechanical properties misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method
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title_full	Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method
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title_sort	improved computer vision framework for mesoscale simulation of xiyu conglomerate using the discrete element method
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title_auth	Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method
abstract	The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns’ surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed for the detection of mesoscale geomaterial structures. Computerized tomography (CT) scan images of the Xiyu conglomerate were employed to establish a high-precision numerical model. From the results of segmentation, the proposed algorithm outperformed UNet, HRNet, and the original SegFormer neural network. The segmentation results were used to calculate the porosity, and biaxial compression numerical simulation experiments based on the real structure were carried out using the particle flow code (PFC). We observed the failure process of the model and obtained the shear strength of the Xiyu conglomerate. We explored the causes and influencing factors of the anisotropy of the Xiyu conglomerate from the microstructure perspective and provide a micro-observation basis for establishing an anisotropic mechanical model.
abstractGer	The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns’ surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed for the detection of mesoscale geomaterial structures. Computerized tomography (CT) scan images of the Xiyu conglomerate were employed to establish a high-precision numerical model. From the results of segmentation, the proposed algorithm outperformed UNet, HRNet, and the original SegFormer neural network. The segmentation results were used to calculate the porosity, and biaxial compression numerical simulation experiments based on the real structure were carried out using the particle flow code (PFC). We observed the failure process of the model and obtained the shear strength of the Xiyu conglomerate. We explored the causes and influencing factors of the anisotropy of the Xiyu conglomerate from the microstructure perspective and provide a micro-observation basis for establishing an anisotropic mechanical model.
abstract_unstemmed	The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns’ surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed for the detection of mesoscale geomaterial structures. Computerized tomography (CT) scan images of the Xiyu conglomerate were employed to establish a high-precision numerical model. From the results of segmentation, the proposed algorithm outperformed UNet, HRNet, and the original SegFormer neural network. The segmentation results were used to calculate the porosity, and biaxial compression numerical simulation experiments based on the real structure were carried out using the particle flow code (PFC). We observed the failure process of the model and obtained the shear strength of the Xiyu conglomerate. We explored the causes and influencing factors of the anisotropy of the Xiyu conglomerate from the microstructure perspective and provide a micro-observation basis for establishing an anisotropic mechanical model.
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title_short	Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method
url	https://doi.org/10.3390/app132413000 https://doaj.org/article/ec34cce9623b43b9b1443e816d5bf7df https://www.mdpi.com/2076-3417/13/24/13000 https://doaj.org/toc/2076-3417
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Improved Computer Vision Framework for Mesoscale Simulation of Xiyu Conglomerate Using the Discrete Element Method

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