scholarly journals Suitability of the Spanish Open Public Cartographic Resources for BIM Site Modeling

2021 ◽  
Author(s):  
Amparo Bernal ◽  
Carlos Muñoz ◽  
Ana Sáez ◽  
Roberto Serrano-López
Keyword(s):  
Spanish Civil War ◽  
Object Representation ◽  
Point Clouds ◽  
Archaeological Site ◽  
Information Modeling ◽  
The Internet ◽  
Topographic Map ◽  
Digital Terrain ◽  
Building Information ◽  
Funerary Architecture

AbstractThe possibility of accessing free cartographic resources on the internet instead of creating their own digital object representation is a big advantage for architecture and engineering professionals in the surveying process of a construction or restoration project. The use of these products must be carried out with the guarantee that the precision of the site representation made from them is adapted to the level of detail required by each phase of the project. This paper compares graphically and statistically the accuracy of the topographic surfaces we can get from the LIDAR point clouds and the Digital Terrain Models (DTMs) of the National Centre for Geographic Information (CNIG by its Spanish acronym) that in Spain are available on the internet free of charge. We will use as a reference surface for comparison, the topographic map of the Digital Model Elevation (DEM) from the images captured by an unmanned aerial vehicle (UAV) processed with software based on algorithms of Structure from Motion (SfM). The study case for this comparative research will be the graphical survey of an archaeological site of funerary architecture made in 1939 during the Spanish Civil War. The monument is located near the city of Burgos (Spain) in an area with difficult access and restricted airspace, which makes difficult the fieldwork. The modeling of the surface has great importance for the graphic survey of the site, because its architectural configuration is adapted to the steep slope of the hillside. The purpose of this research is to check, if the surfaces provided through the Spanish open public cartographic resources are accurate enough to replace the mapping from UAV photogrammetry. Finally, if the outcomes can be extrapolated for widespread use of these resources for the site modeling of a project, according to the requirements of the different levels of development defined in the Building Information Modeling (BIM) methodology.

Significance of Building Information Modeling in Modern Project Management for Sustainable Smart City Applications

2021 ◽  
pp. 2141007
Author(s):  
Mengyi Lian ◽  
Xiaowei Liu
Keyword(s):  
Project Management ◽  
Internet Of Things ◽  
Smart City ◽  
Building Information Modeling ◽  
Information Modeling ◽  
The Internet ◽  
City Environment ◽  
Modern Project ◽  
Building Information ◽  
The Internet Of Things

Building information modeling (BIM) is one of the most exciting recent construction, engineering, and architecture developments. Built environments play a significant role in Smart City worldwide, and they are used to convey useful information to achieve smart city strategic goals. In modern project management, optimizing resources, BIM data integration, and data sharing in a smart city environment is challenging. Hence, in this paper, IoT-based Improved Building Information modeling (IoT-IBIM) has been proposed to overcome the challenges in building information modeling in modern project management for sustainable smart city applications. This paper discusses the efforts to create and integrate built-in environment data with IoT sensors for effective communication. The Internet of Things provides efficient resource control, increased efficiency, and improved human quality of life. As a result, the Internet of Things is a critical enabler of smart societies, including smart homes, smart cities, and smart factories. Building Information Modeling is an advanced asset allocation framework that generates high-quality output, reduces resource use, reduces environmental effects of development, and secures resources and availability for future generations. The experimental results show that the proposed IoT-IBIM method enhances the performance ratio and improves data integration and data sharing in a smart city environment.

scholarly journals INTEGRATING TOPOGRAPHIC, PHOTOGRAMMETRIC AND LASER SCANNING TECHNIQUES FOR A SCAN-TO-BIM PROCESS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 883-890
Author(s):  
M. Lo Brutto ◽  
E. Iuculano ◽  
P. Lo Giudice
Keyword(s):  
Laser Scanning ◽  
Building Information Modeling ◽  
Laser Scanner ◽  
Point Clouds ◽  
Parametric Modeling ◽  
Information Modeling ◽  
Survey Method ◽  
The Real ◽  
Building Information ◽  
Heritage Building

Abstract. The preservation of historic buildings can often be particularly difficult due to the lack of detailed information about architectural features, construction details, etc.. However, in recent years considerable technological innovation in the field of Architecture, Engineering, and Construction (AEC) has been achieved by the Building Information Modeling (BIM) process. BIM was developed as a methodology used mainly for new construction but, given its considerable potential, this approach can also be successfully used for existing buildings, especially for buildings of historical and architectural value. In this case, it is more properly referred to as Historic – or Heritage – Building Information Modeling (HBIM). In the HBIM process, it is essential to precede the parametric modeling phase of the building with a detailed 3D survey that allows the acquisition of all geometric information. This methodology, called Scan-to-BIM, involves the use of 3D survey techniques for the production of point clouds as a geometric “database” for parametric modeling. The Scan-to-BIM approach can have several issues relating to the complexity of the survey. The work aims to apply the Scan-to-BIM approach to the survey and modeling of a historical and architectural valuable building to test a survey method, based on integrating different techniques (topography, photogrammetry and laser scanning), that improves the data acquisition phase. The “Real Cantina Borbonica” (Cellar of Royal House of Bourbon) in Partinico (Sicily, Italy) was chosen as a case study. The work has allowed achieving the HBIM of the “Real Cantina Borbonica” and testing an approach based exclusively on a topographic constraint to merge in the same reference system all the survey data (laser scanner and photogrammetric point clouds).

A Reality Integrated BIM for Architectural Heritage Conservation

2019 ◽  
pp. 142-176
Author(s):  
Fabrizio Ivan Apollonio ◽  
Marco Gaiani ◽  
Zheng Sun
Keyword(s):  
Laser Scanning ◽  
Low Cost ◽  
Point Clouds ◽  
Information Modeling ◽  
Free Form ◽  
Architectural Heritage ◽  
Ideal Model ◽  
Double Curvature ◽  
Building Information ◽  
Architectural Treatises

Building Information Modeling (BIM) has attracted wide interest in the field of documentation and conservation of Architectural Heritage (AH). Existing approaches focus on converting laser scanned point clouds to BIM objects, but laser scanning is usually limited to planar elements which are not the typical state of AH where free-form and double-curvature surfaces are common. We propose a method that combines low-cost automatic photogrammetric data acquisition techniques with parametric BIM objects founded on Architectural Treatises and a syntax allowing the transition from the archetype to the type. Point clouds with metric accuracy comparable to that from laser scanning allows accurate as-built model semantically integrated with the ideal model from parametric library. The deviation between as-built model and ideal model is evaluated to determine if feature extraction from point clouds is essential to improve the accuracy of as-built BIM.

scholarly journals Topology Reconstruction of BIM Wall Objects from Point Cloud Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1800 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Evaluation Framework ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Current State ◽  
Building Information ◽  
Popular Subject ◽  
Best Fit

The processing of remote sensing measurements to Building Information Modeling (BIM) is a popular subject in current literature. An important step in the process is the enrichment of the geometry with the topology of the wall observations to create a logical model. However, this remains an unsolved task as methods struggle to deal with the noise, incompleteness and the complexity of point cloud data of building scenes. Current methods impose severe abstractions such as Manhattan-world assumptions and single-story procedures to overcome these obstacles, but as a result, a general data processing approach is still missing. In this paper, we propose a method that solves these shortcomings and creates a logical BIM model in an unsupervised manner. More specifically, we propose a connection evaluation framework that takes as input a set of preprocessed point clouds of a building’s wall observations and compute the best fit topology between them. We transcend the current state of the art by processing point clouds of both straight, curved and polyline-based walls. Also, we consider multiple connection types in a novel reasoning framework that decides which operations are best fit to reconstruct the topology of the walls. The geometry and topology produced by our method is directly usable by BIM processes as it is structured conform the IFC data structure. The experimental results conducted on the Stanford 2D-3D-Semantics dataset (2D-3D-S) show that the proposed method is a promising framework to reconstruct complex multi-story wall elements in an unsupervised manner.

scholarly journals Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR

2020 ◽  
Vol 5 (7) ◽  
pp. 55 ◽  
Author(s):  
Luigi Barazzetti ◽  
Mattia Previtali ◽  
Marco Scaioni
Keyword(s):  
Autonomous Vehicle ◽  
Point Clouds ◽  
Traffic Monitoring ◽  
Information Modeling ◽  
Time Data ◽  
Cloud Processing ◽  
Road Geometry ◽  
Building Information ◽  
Project File ◽  
Autonomous Vehicle Navigation

Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others. Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment. This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects. The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail. The model is also refined by adding parametric elements for buildings and vegetation. Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data. This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing.

scholarly journals Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields

2019 ◽  
Vol 11 (13) ◽  
pp. 1586 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry ◽  
Balanced Clustering

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the labeled segments should be clustered according to their respective objects. The current processes to perform this task are sensitive to noise, occlusions, and the associativity between faces of neighboring objects. The proper retrieval of the observed geometry is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each wall segment in order to determine which wall it belongs to. First, a set of classified planar primitives is obtained through algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph. The method is tested on our own data as well as the 2D-3D-Semantics (2D-3D-S) benchmark data of Stanford. Compared to a conventional region growing method, the proposed method reduces the rate of false positives, resulting in better wall clusters. Overall, the method computes a more balanced clustering of the observations. A key advantage of the proposed method is its capability to deal with wall geometry in complex configurations in multi-storey buildings opposed to the presented methods in current literature.

scholarly journals Automatic 2D Floorplan CAD Generation from 3D Point Clouds

2020 ◽  
Vol 10 (8) ◽  
pp. 2817 ◽  
Author(s):  
Uuganbayar Gankhuyag ◽  
Ji-Hyeong Han
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Automated Method ◽  
3D Point Clouds ◽  
Building Information ◽  
Geometric Quality

In the architecture, engineering, and construction (AEC) industry, creating an indoor model of existing buildings has been a challenging task since the introduction of building information modeling (BIM). Because the process of BIM is primarily manual and implies a high possibility of error, the automated creation of indoor models remains an ongoing research. In this paper, we propose a fully automated method to generate 2D floorplan computer-aided designs (CADs) from 3D point clouds. The proposed method consists of two main parts. The first is to detect planes in buildings, such as walls, floors, and ceilings, from unstructured 3D point clouds and to classify them based on the Manhattan-World (MW) assumption. The second is to generate 3D BIM in the industry foundation classes (IFC) format and a 2D floorplan CAD using the proposed line-detection algorithm. We experimented the proposed method on 3D point cloud data from a university building, residential houses, and apartments and evaluated the geometric quality of a wall reconstruction. We also offer the source code for the proposed method on GitHub.

scholarly journals IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS

2018 ◽  
Vol IV-2 ◽  
pp. 33-39 ◽  
Author(s):  
M. Bassier ◽  
R. Klein ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Initial Point ◽  
Point Clouds ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.<br>In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

scholarly journals Method for clustering and identification of objects in laser scanning point clouds using dynamic logic

2021 ◽  
Author(s):  
Yevgeny Milanov ◽  
Vladimir Badenko ◽  
Vladimir Yadykin ◽  
Leonid Perlovsky
Keyword(s):  
Field Theory ◽  
Laser Scanning ◽  
Dynamic Logic ◽  
Point Clouds ◽  
Information Modeling ◽  
Digital Data ◽  
Neural Modeling ◽  
Multiple Sources ◽  
Building Information ◽  
Spatial Coordinates

Abstract Today there is a gap between a presence of various new equipment on the market which provides streams of various digital data about the environment, in particular in the form of laser scanning point clouds, and the lack of adequate efficient methods and software for information extraction from such data. A solution to the problem of bridging this gap is proposed on the basis of neural modeling field theory and dynamic logic (DL). We present a DL-based method of extracting and analyzing information from hybrid point clouds, which include not only spatial coordinates and intensity, but also the color of each point, and can be from multiple sources including terrestrial, mobile and airborne laser scanning data. The proposed method is significant for creating a fundamental theoretical basis for new application algorithms and software for many new applications, including building information modeling, “smart city” environment, etc. The proposed method is fairly new to solving various problems related to extracting semantically rich information from a nontraditional type of digital data, especially hybrid point clouds created from laser scanning. This method will allow to significantly expand the existing boundaries of knowledge in the field of extraction and analysis of information from various digital data, because neural modeling field theory and DL can improve the performance of relevant calculations and close the existing gap in analysis of digital images.

scholarly journals GEOMETRY AND INFORMATION FOR THE PRESERVATION OF A ROMAN MOSAIC THROUGH A HBIM APPROACH

2021 ◽  
Vol VIII-M-1-2021 ◽  
pp. 73-79
Author(s):  
D. Del Pozzo ◽  
B. Scala ◽  
A. Adami
Keyword(s):  
Building Information Modeling ◽  
Archaeological Site ◽  
Good Health ◽  
Interesting Case ◽  
Information Modeling ◽  
Design Phase ◽  
Historical Building ◽  
Roof Structure ◽  
Building Information ◽  
The Cost

Abstract. The archaeological site is a mine of data and information that helps to deepen the knowledge of its origin, history, and structure. This virtuous approach becomes even more effective when these data, properly processed and structured, form the basis for a project of conservation and enhancement of the cultural asset.The Roman mosaics dug in Castiglione delle Stiviere in 1995 represent an interesting case in which all the archaeological information, made available by the Superintendence, was used through an HBIM (Historical Building Information Modeling) approach for the conservation project. The Stratigraphic Units (US) of the findings have identified the strategy for the geometric and informative modeling of the BIM (Building Information Modeling) model and have also been exploited in the design phase for the project of the new roof structure and especially for the cost analysis. The structuring of the data by stratigraphic units was also used in the drafting of the preventive and planned conservation, necessary to enhance and prolong the state of good health of the property.This work has been developed in the internship activity within a training course on HBIM, in collaboration with the Diocese of Mantua, owner of the property.

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