Cargando…
Determination of roughness coefficient in 3D digital representations of rocks
The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out t...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233708/ https://www.ncbi.nlm.nih.gov/pubmed/35752655 http://dx.doi.org/10.1038/s41598-022-15030-y |
_version_ | 1784735862302441472 |
---|---|
author | Scalco, Leonardo Tonietto, Leandro Velloso, Raquel Quadros Racolte, Graciela Gonzaga, Luiz Veronez, Mauricio Roberto |
author_facet | Scalco, Leonardo Tonietto, Leandro Velloso, Raquel Quadros Racolte, Graciela Gonzaga, Luiz Veronez, Mauricio Roberto |
author_sort | Scalco, Leonardo |
collection | PubMed |
description | The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters ([Formula: see text] ) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values ([Formula: see text] ) of [Formula: see text] and [Formula: see text] were obtained for samples 1 and 2, respectively. By the same method, the results of the [Formula: see text] coefficient applied three-dimensionally over the entire rocky surface were at most [Formula: see text] and [Formula: see text] , respectively, showing the difference in values along the surface and the importance of this approach. |
format | Online Article Text |
id | pubmed-9233708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92337082022-06-27 Determination of roughness coefficient in 3D digital representations of rocks Scalco, Leonardo Tonietto, Leandro Velloso, Raquel Quadros Racolte, Graciela Gonzaga, Luiz Veronez, Mauricio Roberto Sci Rep Article The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters ([Formula: see text] ) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values ([Formula: see text] ) of [Formula: see text] and [Formula: see text] were obtained for samples 1 and 2, respectively. By the same method, the results of the [Formula: see text] coefficient applied three-dimensionally over the entire rocky surface were at most [Formula: see text] and [Formula: see text] , respectively, showing the difference in values along the surface and the importance of this approach. Nature Publishing Group UK 2022-06-25 /pmc/articles/PMC9233708/ /pubmed/35752655 http://dx.doi.org/10.1038/s41598-022-15030-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Scalco, Leonardo Tonietto, Leandro Velloso, Raquel Quadros Racolte, Graciela Gonzaga, Luiz Veronez, Mauricio Roberto Determination of roughness coefficient in 3D digital representations of rocks |
title | Determination of roughness coefficient in 3D digital representations of rocks |
title_full | Determination of roughness coefficient in 3D digital representations of rocks |
title_fullStr | Determination of roughness coefficient in 3D digital representations of rocks |
title_full_unstemmed | Determination of roughness coefficient in 3D digital representations of rocks |
title_short | Determination of roughness coefficient in 3D digital representations of rocks |
title_sort | determination of roughness coefficient in 3d digital representations of rocks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233708/ https://www.ncbi.nlm.nih.gov/pubmed/35752655 http://dx.doi.org/10.1038/s41598-022-15030-y |
work_keys_str_mv | AT scalcoleonardo determinationofroughnesscoefficientin3ddigitalrepresentationsofrocks AT toniettoleandro determinationofroughnesscoefficientin3ddigitalrepresentationsofrocks AT vellosoraquelquadros determinationofroughnesscoefficientin3ddigitalrepresentationsofrocks AT racoltegraciela determinationofroughnesscoefficientin3ddigitalrepresentationsofrocks AT gonzagaluiz determinationofroughnesscoefficientin3ddigitalrepresentationsofrocks AT veronezmauricioroberto determinationofroughnesscoefficientin3ddigitalrepresentationsofrocks |