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

Building Information Modelling is a well-known acronym in the construction industry. BIM process is more than modelling buildings, and it provides the opportunity to drive efficiency and effectiveness to the information management of build projects. Accordingly, Building Information Models (BIMs), typically known as semantic three-dimensional parametric models, are fast becoming the comprehensive information source in Architecture, Engineering and Construction (AEC), and Facility Management (FM). The use of BIM in existing buildings has been hampered by the challenges and limitations surrounding the available technologies. The most popular and commonly used approach for generating models is to manually generate 3D artefacts utilizing point measurements extracted from range-based technologies (typically 3D laser scanning). In the recent past, several studies have been carried out to make the retro t BIM development process as effective and efficient as possible by developing different methods for mapping 3D models using Point Cloud Data (PCD) as the main source of information. However, an appropriate fully generated parametric model is still some way away. In this paper, we review the-state-of-the-art to address the research gap and challenges involved in generating parametric models before outlining the proposal of our approach. In this research, we employ Semantic Web technologies to capture parametric models. Elements are first recognized in PCD, and corresponding geometric information extracted from PCD are then tagged with Universally Unique Identifiers (UUIDs). Tags are then linked with the generated Resource Description Framework (RDF) data for each element. The core and challenging part of this research is the standardization process where RDF as a serialization is translated to Industry Foundation Classes (IFC) as a data model. The generated IFC format is then utilized to capture corresponding models. The primary results are very promising and should be of interest to the modelling of all kinds of building components, particularly historical building information modelling (HBIM).

Semantic-rich 3D parametric models, like Building Information Models (BIMs) are becoming the main information source during the entire lifespan of an asset. The use of BIM in existing buildings has been hampered by the challenges surrounding the limitations of existing technologies for developing retrofit models. Some progress has been recently made in generating non-parametric models from the Point Cloud Data (PCD). However, a proper fully developed parametric model is still some way away. In this paper, challenges are addressed by reviewing the state-of-the-art before presenting our approach. The aim of our approach is to apply the Semantic Web Technologies for generating parametric models using PCD as primary data. The Semantic Web as a set of standards and technologies is used for providing an appropriate framework for storing, sharing, and reusing the semantics of information on the web. Building elements are recognized in PCD, and the concept of Resource Description Framework (RDF) as a Semantic Web technology and a standard model for interchanging the data on the web is then used to markup detected elements. The RDF data is then standardized to Industry Foundation Classes (IFC) as an open standard building data model to generate the parametric model of the asset utilizing BIM software that supports IFC. Some parts of this ongoing research are performed manually, and the future work is to implement the process automatically. Primary results are quite promising and should be of interest to the modeling of all kinds of assets, in particular, Historical Building Information Modelling (HBIM).

Cultural heritage conservation and restoration work is a complex process that includes documentation, data collection, interpretation and production. By integrating the BIM (Building Information Modeling) methodology into this process, the concept of HBIM (Historic Building Information Modeling) has been formed. In this article, the progress of this process with the HBIM methodology in the restoration, protection and management studies of cultural heritage buildings and sites requiring collective and holistic work, the evaluation of access to the information obtained, and the provision of interdisciplinary information exchange were analyzed. By using the SWOT analysis method, the opportunities and threats offered by using the HBIM working methodology were evaluated and the strengths and weaknesses of the HBIM technology were determined. As a result of the SWOT analysis, it has been determined that the HBIM application will positively contribute to the heritage structures and increase in the direction of potential opportunities by eliminating the weaknesses.

Architectural heritage surveying plays a fundamental role in the preservation of historic buildings for scientific research, education and tourism. The use of Unmanned Aerial Vehicles and Terrestrial Laser Scanning techniques are essential for architectural heritage surveying and mapping. In recent years, the combination of Building Information Modelling (BIM) with heritage studies has been presented as Historic BIM (HBIM) which, integrated with UVA and TLS, is a technique that is able to deal more efficiently with the management and protection of historic buildings. This paper focuses on the integration of UVA images and point clouds from laser scanning to build a 3D architectural model for the documentation of Chinese historic buildings. In particular, the method, tested in the case study of the traditional village, Hexinwu, China, can contribute further to the analysis, evaluation and heritage planning of this remarkable architectural structure, thus increasing its historical significance for the future.

PurposeThe purpose of this research is to outline in detail the procedure of remote data capture using laser scanning and the subsequent processing required in order to identify a new methodology for creating full engineering drawings (orthographic and 3D models) from laser scan and image survey data for historic structures.Design/methodology/approachHistoric building information modelling (HBIM) is proposed as a new system of modelling historic structures; the HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital cameras. A range of software programs is then used to combine the image and scan data.FindingsMeshing of the point cloud followed by texturing from the image data creates a framework for the creation of a 3D model. Mapping of BIM objects onto the 3D surface model is the final stage in the reverse engineering process, creating full 2D and 3D models including detail behind the object's surface concerning its methods of construction and material makeup, this new process is described as HBIM.Originality/valueThe future research within this area will concentrate on three main stands. The initial strand is to attempt improve the application of geometric descriptive language to build complex parametric objects. The second stand is the development of a library of parametric based on historic data (from Vitruvius to 18th century architectural pattern books). Finally, while it is possible to plot parametric objects onto the laser scan data, there is need to identify intermediate software platforms to accelerate this stage within the HBIM framework.

With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point‐wise classification strategy, which is not efficient for processing large‐volume TLS datasets acquired at the forest plot level. In this study, we proposed a segment‐wise classification strategy to improve the efficiency of the wood–leaf separation from large‐volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment‐wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state‐of‐the‐art wood–leaf separation methods, that is, the CANUPO and LeWoS. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy. Our study demonstrates that the segment‐wise classification strategy applies to the large‐volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.

The design and evaluation of virtual learning environments for construction and surveying students is presented in this paper; by combining virtual learning environment and on-site student surveys to model and replicate practice in the architectural heritage sector. The Virtual Learning Environment is enhanced with real live survey projects whereby students collect the data to build virtual historic buildings from onsite surveys using advanced survey equipment. The survey data is modelled in HBIM; Historic Building Information Modelling (HBIM) is currently being developed as a virtual learning tool for construction and surveying students in the Dublin Institute of Technology. HBIM, is a novel solution whereby interactive parametric objects representing architectural elements are constructed from historic data, these elements, including detail behind the scan surface are accurately mapped onto a laser or image based survey. The architectural elements are scripted using a Geometric Descriptive Language GDL. In the case of this project a Virtual Learning Environment is being developed which combines advanced recording and surveying with Building Information Modelling (BIM) to simulate and analyse existing buildings.

An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.

Recent technological evolutions in the acquisition and management of building data are offering new opportunities for digital reconstruction. At the same time, the BIM (Building Information Modeling) methodology, based on the implementation of libraries composed of parametric objects provided by the IFC (Industry Foundation Classes) standard, allows the design and management of data of existing buildings, and, in particular, historical and archaeological buildings. In the latter case, the great variety of Cultural Heritage (CH) distributed over the European territory, and the ability of BIM to cover the life of buildings or/and other artefacts from a geometric, descriptive, physical and static point of view, have stimulated the development of the HBIM (Historic BIM) modelling. The HBIM approach should consider the complexity of historical or archaeological buildings or artefacts, with particular attention to possible fragmentation or incompleteness of parts. In this work, different approaches regarding the survey, restitution and data management will be described, finalised to the construction of an HBIM model, considering different possible variables, emerging from different study cases.

Abstract. The implementation of historical information within BIM (Building Information Modelling) platforms has experienced great development processes during last years, generating excellent studies based on Historic Building Information Modelling (Murphy et al., 2009; 2013). The HBIM developing growth is certainly explained due to advantages concerning the documentation step as well as monitoring operations for Cultural Heritage assets. In this sense, information concerning historical architectures can be extracted directly from walls and masonries and it is related to stratigraphic information derived from archaeological analysis: this kind of analysis is fundamental in order to comprehend the evolution of the construction site through the identification of layers due to modifications and actions (Parenti R., 2000).The inclusion of stratigraphic analysis inside a HBIM workflow could be an innovative point as far as the management and monitoring is concerned. This kind of documentation, that was not designed to be included inside a common BIM platform, could be collected coupled with digital metric information derived from metric surveys even if it is still considered an ongoing research field, especially since Cultural Heritage assets have no BIM standard classification. For this reason, the main goal of this research is to adapt the possibilities of open source solutions concerning BIM methodologies to building archaeology documentation and analysis exploring unconventional strategies and also overcoming 3D modelling limitations of BIM software with free form modeler based on NURBS algorithm (Oreni et al., 2014), developing a particular scan-to-BIM process that, owing to the used opens source solutions and algorithm, can be renamed scan-to-openBIM via NURBS.

The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.

Knowledge about heritage buildings and structures is interesting for a wide variety of stakeholders,
\nranging from architects to operators or the public administration. Such knowledge includes a large
\nvariety of physical and functional characteristics of the building. Laser scanning allows efficient and
\naccurate 3D digitalization of heritage sites and subsequent data processing towards the creation of
\ngeometrically and semantically rich models.
\nParameterization and semantic enrichment of heritage building representations towards the creation
\nof Heritage Building Information Models (HBIM) generally involves the use of point clouds
\nand images as templates for manual mapping procedures in commercial software. Indeed, the information
\nto include in BIM models depends on the requirements of the application it is intended to
\nserve. In view of that, the applications presented in this chapter pertain to automated techniques
\nthat were implemented to parameterize point clouds towards models suitable for energy analysis
\npurposes.
\nThe challenge in automating the reconstruction of heritage buildings is to deal with their geometrical
\ncomplexity and irregularity, meaning that the methodologies selected should be robust and
\nefficient under these conditions. The resulting 3D semantically rich model enhances the knowledge
\nof the heritage building, complementing other representations of the facility such as point clouds or
\nhandmade HBIM. The procedure is implemented and validated in a real case study: the Ducal Palace
\nin Guimarães (Portugal).

Nowadays precast concrete elements are widely used in buildings, bridges, and other civil infrastructure facilities because precast elements allow rapid construction and high precision quality control. However, the quality inspection of precast concrete elements primarily relies on manual inspection, which is time consuming and error-prone. Recently, terrestrial laser scanner (TLS) has been used to improve quality inspection. The authors’ group has previously developed an automated dimension estimation technique for precast concrete elements, but its applicability is limited only to precast elements with rectangular shapes. This study advances our previous work so that the dimensions of precast elements with irregular shapes can also be automatically estimated. First, a density-based clustering algorithm is adopted to extract target objects from 3D point cloud data acquired by a TLS. Then, coarse registration is conducted to match each object extracted from the 3D point cloud with the asdesign objects in building information models (BIM) one by one. Thirdly, all point cloud data points are registered onto different surfaces of the as-designed objects in BIM through fine registration. Lastly, the as-built dimensions of the precast concrete element are extracted and compared with the as-design ones in BIM. The effectiveness and accuracy of the developed technique are examined using point cloud data obtained from a laboratory-scale precast concrete bridge deck panel.

The management of cultural landscapes with protected historic buildings faces challenges due to these structures’ unique characteristics and scale. Semantically enriched 3D modeling methods, like Historic Building Information Modeling (HBIM) integrated with Historic Geographic Information Systems (HGIS), offer a multi-scale approach to documenting and conserving cultural heritage, from building components to territorial levels. This study explores the integration of HBIM-HGIS for multi-scale digital documentation of architectural heritage, focusing on modern architecture designed by Oscar Niemeyer in 1944, located in Belo Horizonte, Brazil. An exploratory qualitative approach is used to create a highly detailed model of the ensemble, utilizing point clouds generated by terrestrial laser scanning and drone photogrammetry as data for model development in Revit. The geometric and semantic information of the model is then exported in IFC format and integrated into a Geodatabase, following the integration of HBIM and HGIS. The study demonstrates that a 3D semantic model integrating HBIM with HGIS enhances preservation efforts. It enables detailed analyses for decision-making and offers a comprehensive understanding of heritage across scales, including landscapes. This holistic approach supports the effective conservation of architectural heritage by unifying tangible and intangible data within a single platform.

Abstract. Geomatics' interest in Building Information Modelling (BIM) processes applied to Cultural Heritage is evolving in many directions. The traditional fields of Geomatics -data acquisition and processing- have been largely dealt with about HBIM (Historical Building Information Modelling) systems, but there are still some issues that need to be explored in more detail. In addition to modelling, it is essential to consider the information content of the model, how it is generated, recorded and managed.If the objective of the HBIM model is the conservation project, it becomes essential to understand what types of data can be inserted in the model, how to record them and, above all, what their purpose is. The HBIM approach did not start out as a simple data repository, but as an information tool with the aim of helping the designer at all stages of the construction process. Moving to the field of preservation, much information about the building is represented by thematic maps. They allow to have a graphical image of the state of conservation of a façade or to understand the structural situation of a building.This research, tested on the Arch of Augustus in Aosta, starts from the big amount of data acquired by the RAVA Laboratory of the Superintendency of Aosta during a long period. These data allowed to test different approaches to thematic mapping, according to the specific themes to be represented (previous restoration interventions, diagnostics, decay mapping, etc). Anyway, this experiment also required a theoretical reasoning that preceded the operational phases. Faced with a new system, in fact, it is always advisable to reason about the method applied, to avoid the error of simply translating a method that could instead be developed in new directions. The question, to which this article wants only to begin answering, concerns the role of thematic mappings in the preservation design made by a HBIM approach, their necessity and their implementation towards a truly three-dimensional data, which thus maintains all the information that is acquired directly in three dimensions and which instead, today, are reprojected in 2D or used as a placeholder in the 3D space.