Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data

Builders of the past naturally adjusted geometries to fit existing surfaces. Today, replicating these forms during the 3D digitization of historical elements poses a significant challenge for BIM operators. Achieving a precise fit for the geometry of a cross-vault facilitates the implementation of t...

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Detalles Bibliográficos
Autores: Moyano, Juan, Barazzetti, Luigi, Previtali, Mattia, Nieto Julián, Juan Enrique
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/174582
Acceso en línea:https://hdl.handle.net/11441/174582
https://doi.org/10.1016/j.autcon.2025.106274
Access Level:acceso abierto
Palabra clave:BIM
Best fit for complex geometries
Parametric 3D models
Point cloud element classification
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spelling Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud dataMoyano, JuanBarazzetti, LuigiPrevitali, MattiaNieto Julián, Juan EnriqueBIMBest fit for complex geometriesParametric 3D modelsPoint cloud element classificationBuilders of the past naturally adjusted geometries to fit existing surfaces. Today, replicating these forms during the 3D digitization of historical elements poses a significant challenge for BIM operators. Achieving a precise fit for the geometry of a cross-vault facilitates the implementation of the Scan-to-BIM approach for repetitive objects with significant variations in their geometry. This paper introduces a descriptive mathematical model that provides BIM experts with a foundation for creating multiple geometric replicas. The approach employs clustering algorithms, optimization techniques, frequency analysis via Fourier transform, and ordinary Kriging interpolation. Two parametric BIM models are developed: one simple model defined by five variables and another more complex model defined by nine geometric variables. Both models are validated against the segmented point cloud. The results indicate interpolated standard deviations of ±0.0085 m for the simple vault and ± 0.0066 m for the complex vault. The difference between using the simple and complex vault models is ±0.0082 m, representing a variation of 0.01 % in the values of the five optimized parameters.ElsevierExpresión Gráfica e Ingeniería en la EdificaciónTEP970: Innovación Tecnológica, Sistemas de Modelado 3d y Diagnosis Energética en Patrimonio y EdificaciónUniversidad de SevillaMinisterio de Ciencia, Innovación y Universidades (MICIU). España2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/174582https://doi.org/10.1016/j.autcon.2025.106274reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésAutomation in Construction, 176, 106274.PID2023-147622OB-I00https://www.sciencedirect.com/science/article/pii/S0926580525003140?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1745822026-06-17T12:51:07Z
dc.title.none.fl_str_mv Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
title Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
spellingShingle Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
Moyano, Juan
BIM
Best fit for complex geometries
Parametric 3D models
Point cloud element classification
title_short Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
title_full Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
title_fullStr Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
title_full_unstemmed Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
title_sort Optimizing best-fit algorithms for complex cross-vault geometries in HBIM generation using point cloud data
dc.creator.none.fl_str_mv Moyano, Juan
Barazzetti, Luigi
Previtali, Mattia
Nieto Julián, Juan Enrique
author Moyano, Juan
author_facet Moyano, Juan
Barazzetti, Luigi
Previtali, Mattia
Nieto Julián, Juan Enrique
author_role author
author2 Barazzetti, Luigi
Previtali, Mattia
Nieto Julián, Juan Enrique
author2_role author
author
author
dc.contributor.none.fl_str_mv Expresión Gráfica e Ingeniería en la Edificación
TEP970: Innovación Tecnológica, Sistemas de Modelado 3d y Diagnosis Energética en Patrimonio y Edificación
Universidad de Sevilla
Ministerio de Ciencia, Innovación y Universidades (MICIU). España
dc.subject.none.fl_str_mv BIM
Best fit for complex geometries
Parametric 3D models
Point cloud element classification
topic BIM
Best fit for complex geometries
Parametric 3D models
Point cloud element classification
description Builders of the past naturally adjusted geometries to fit existing surfaces. Today, replicating these forms during the 3D digitization of historical elements poses a significant challenge for BIM operators. Achieving a precise fit for the geometry of a cross-vault facilitates the implementation of the Scan-to-BIM approach for repetitive objects with significant variations in their geometry. This paper introduces a descriptive mathematical model that provides BIM experts with a foundation for creating multiple geometric replicas. The approach employs clustering algorithms, optimization techniques, frequency analysis via Fourier transform, and ordinary Kriging interpolation. Two parametric BIM models are developed: one simple model defined by five variables and another more complex model defined by nine geometric variables. Both models are validated against the segmented point cloud. The results indicate interpolated standard deviations of ±0.0085 m for the simple vault and ± 0.0066 m for the complex vault. The difference between using the simple and complex vault models is ±0.0082 m, representing a variation of 0.01 % in the values of the five optimized parameters.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/174582
https://doi.org/10.1016/j.autcon.2025.106274
url https://hdl.handle.net/11441/174582
https://doi.org/10.1016/j.autcon.2025.106274
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Automation in Construction, 176, 106274.
PID2023-147622OB-I00
https://www.sciencedirect.com/science/article/pii/S0926580525003140?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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