Research on Historic Bim of Built Heritage in Taiwan – A Case Study of Huangxi Academy

Lu, Yung Chi; Shih, Tian‐Yuan; Yen, Ya-Ning

doi:10.5194/isprs-archives-xlii-2-615-2018

Cited by 10 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Autodesk Revit, a common BIM platform, provides friendly support of point clouds and becomes a hot as-built HBIM modeling environment [18,19,22,37,54]. Autodesk Revit also includes a built-in Dynamo visual programming tool to extend the BIM capabilities [42,[55][56][57][58]. Autodesk Dynamo is an open-source visual programming environment, which offers designers the ability to design and manipulate BIM elements by programming interactively.…”

Section: Methodsmentioning

confidence: 99%

HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation

Yang

Murtiyoso

et al. 2019

IJGI

View full text Add to dashboard Cite

Built heritage has been documented by reality-based modeling for geometric description and by ontology for knowledge management. The current challenge still involves the extraction of geometric primitives and the establishment of their connection to heterogeneous knowledge. As a recently developed 3D information modeling environment, building information modeling (BIM) entails both graphical and non-graphical aspects of the entire building, which has been increasingly applied to heritage documentation and generates a new issue of heritage/historic BIM (HBIM). However, HBIM needs to additionally deal with the heterogeneity of geometric shape and semantic knowledge of the heritage object. This paper developed a new mesh-to-HBIM modeling workflow and an integrated BIM management system to connect HBIM elements and historical knowledge. Using the St-Pierre-le-Jeune Church, Strasbourg, France as a case study, this project employs Autodesk Revit as a BIM environment and Dynamo, a built-in visual programming tool of Revit, to extend the new HBIM functions. The mesh-to-HBIM process segments the surface mesh, thickens the triangle mesh to 3D volume, and transfers the primitives to BIM elements. The obtained HBIM is then converted to the ontology model to enrich the heterogeneous knowledge. Finally, HBIM geometric elements and ontology semantic knowledge is joined in a unified BIM environment. By extending the capability of the BIM platform, the HBIM modeling process can be conducted in a time-saving way, and the obtained HBIM is a semantic model with object-oriented knowledge.

show abstract

Section: Methodsmentioning

confidence: 99%

HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation

Yang

Murtiyoso

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…Recent work [48] developed a new workflow for mesh modelling towards HBIM, for which Autodesk Revit (BIM) and Dynamo are used to explore new HBIM features. However, several studies [49,50] implement these tools for specific objects, but not for building construction units. Finally, the work by Tommasi et al [51] brings together diverse software in the market for reverse engineering in BIM applied to historical elements, whereas Pocobelli et al [52] integrated organised moisture data into spreadsheets and associated it with parametric objects development through Dynamo. In addition, there are different reconstruction methods in the scientific literature [53][54][55][56][57][58][59][60][61], most of which use the Delaunay triangulation [54].…”

Section: Related Workmentioning

confidence: 99%

From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

et al. 2020

View full text Add to dashboard Cite

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

show abstract

“…Algorithmic segmentation uses mathematical rules and functions as constraints during the segmentation (and possibly also classification). These rules may range from simple rules (e.g., "floors are flat and located below each storey" or "pillars are cylinders") [25] to the implementation of ontological relations [41][42][43].…”

Section: Algorithmic Approachmentioning

confidence: 99%

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Murtiyoso

Grussenmeyer

2020

Sensors

View full text Add to dashboard Cite

3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.

show abstract

Research on Historic Bim of Built Heritage in Taiwan – A Case Study of Huangxi Academy

Cited by 10 publications

References 15 publications

HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation

HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation

From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Contact Info

Product

Resources

About