scholarly journals Automatic Space Analysis Using Laser Scanning and a 3D Grid: Applications to Industrial Plant Facilities

2020 ◽  
Vol 12 (21) ◽  
pp. 9087
Author(s):  
Donghyun Kim ◽  
Soonwook Kwon ◽  
Chung-Suk Cho ◽  
Borja García de Soto ◽  
Daeyoon Moon
Keyword(s):  
Laser Scanning ◽  
Complex Structure ◽  
Information Modeling ◽  
Industrial Plant ◽  
Cloud Data ◽  
Space Analysis ◽  
Building Information ◽  
Grid Applications ◽  
Floor Plans ◽  
Point Data

While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, etc.,). Furthermore, problems during installation, operation, and maintenance stages caused by inconsistencies between floor plans and actual layout are on the rise. Although some of these conflicts can be addressed through clash detection using BIM (building information modeling), quality BIM models are scarce, especially for existing industrial plants. This study proposes a way to address the complexities caused by changes during plant construction and securing space for the installation of equipment during the construction and lifecycle of built facilities. 3D cloud point data of space and equipment were collected using 3D laser scanning to conduct space matching. In processing the space matching, data were simplified by applying the 3D grid and by comparing the data, easier identification of the space for target equipment was accomplished. This study also proposed a pre-processing method based on sub-sampling that optimizes the point cloud data and verifies the processing speed and accuracy. Lastly, it finds free space for various equipment layouts required in industrial plant projects by space analysis, proposed algorithms, and processes for obtaining the coordinates of valid space for equipment arrangement. The proposed method of this study is expected to help solve the problems derived from arrangement and installation of new equipment in a complex plant site.

scholarly journals 3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

2020 ◽  
Vol 12 (14) ◽  
pp. 2301
Author(s):  
Mario Soilán ◽  
Andrés Justo ◽  
Ana Sánchez-Rodríguez ◽  
Belén Riveiro
Keyword(s):  
Information Exchange ◽  
Point Cloud ◽  
Laser Scanning ◽  
Information Modeling ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Cloud Processing ◽  
Building Information ◽  
Definition Of ◽  
Key Steps

Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data.

scholarly journals A Scan-to-BIM Methodology Applied to Heritage Buildings

Heritage ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 47-67 ◽  
Author(s):  
Rocha ◽  
Mateus ◽  
Fernández ◽  
Ferreira
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Information Modeling ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Data Alignment ◽  
Building Information ◽  
Heritage Buildings ◽  
Restoration Work

Heritage buildings usually have complex (non-parametric) geometries that turn their digitization through conventional methods in inaccurate and time-consuming processes. When it comes to the survey and representation of historical assets, remote sensing technologies have been playing key roles in the last few years: 3D laser scanning and photogrammetry surveys save time in the field, while proving to be extremely accurate at registering non-regular geometries of buildings. However, the efficient transformation of remote-sensing data into as-built parametric smart models is currently an unsolved challenge. A pragmatic and organized Historic Building Information Modeling (HBIM) methodology is essential in order to obtain a consistent model that can bring benefits and integrate conservation and restoration work. This article addresses the creation of an HBIM model of heritage assets using 3D laser scanning and photogrammetry. Our findings are illustrated in one case study: The Engine House Paços Reais in Lisbon. The paper first describes how and what measures should be taken to plan a careful scan-to-HBIM process. Second, the description of the remote-sensing survey campaign is conducted accordingly and is aimed at a BIM output, including the process of data alignment, cleaning, and merging. Finally, the HBIM modeling phase is described, based on point cloud data.

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 Non-Invasive Identification of Vulnerability Elements in Existing Buildings and Their Visualization in the BIM Model for Better Project Management: The Case Study of Cuccagna Farmhouse

2020 ◽  
Vol 10 (6) ◽  
pp. 2119 ◽  
Author(s):  
Elsa Garavaglia ◽  
Anna Anzani ◽  
Fabio Maroldi ◽  
Fabio Vanerio
Keyword(s):  
Laser Scanning ◽  
Invasive Procedure ◽  
Information Modeling ◽  
Seismic Action ◽  
Existing Buildings ◽  
Modeling Methodology ◽  
Mediterranean Countries ◽  
Building Information ◽  
Entire Complex ◽  
The Mediterranean

Due to the conjunction between the European and African plates, complex tectonic phenomena take place in the Mediterranean basin. These phenomena cause more or less violent seismic resentments in the countries facing the basin itself. The diffused built historical heritage, characteristic of villages in the Mediterranean countries, is the most vulnerable toward seismic action, and in case of a catastrophic event can cause the loss of human lives. In Italy, the protection of historic buildings is a significant issue, and many regions promoted policies to ensure the safety of the diffused built heritage. Research groups work in synergy to develop procedures for the vulnerability assessment of existing buildings and to define appropriate action plans. This research presents a little or not at all invasive procedure for investigating vulnerability. This procedure is easily replicable and able to support techniques already in use with innovative aspects such as laser scanning of the entire complex and visual identification of vulnerable elements through the BIM (building information modeling) methodology. The procedure applicability is shown in the study of a Milanese farmhouse that has been financed by Fondazione CARIPLO, Bandi 2017 Arte e Cultura-Beni culturali a rischio, Project PRE.CU.R.S.OR.

scholarly journals ABGS: A System for the Automatic Generation of Building Information Models from Two-Dimensional CAD Drawings

2020 ◽  
Vol 12 (17) ◽  
pp. 6713
Author(s):  
Youngsoo Byun ◽  
Bong-Soo Sohn
Keyword(s):  
Computer Aided Design ◽  
Low Cost ◽  
3D Models ◽  
Automatic Generation ◽  
Information Modeling ◽  
Two Dimensional ◽  
Efficient Design ◽  
Information Models ◽  
Building Information ◽  
Floor Plans

Building Information Modeling (BIM) refers to 3D-based digital modeling of buildings and infrastructure for efficient design, construction, and management. Governments have recognized and encouraged BIM as a primary method for enabling advanced construction technologies. However, BIM is not universally employed in industries, and most designers still use Computer-Aided Design (CAD) drawings, which have been used for several decades. This is because the initial costs for setting up a BIM work environment and the maintenance costs involved in using BIM software are substantially high. With this motivation, we propose a novel software system that automatically generates BIM models from two-dimensional (2D) CAD drawings. This is highly significant because only 2D CAD drawings are available for most of the existing buildings. Notably, such buildings can benefit from the BIM technology using our low-cost conversion system. One of the common problems in existing methods is possible loss of information that may occur during the process of conversion from CAD to BIM because they mainly focus on creating 3D geometric models for BIM by using only floor plans. The proposed method has an advantage of generating BIM that contains property information in addition to the 3D models by analyzing floor plans and other member lists in the input design drawings together. Experimental results show that our method can quickly and accurately generate BIM models from 2D CAD drawings.

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

2020 ◽  
Vol 12 (7) ◽  
pp. 1094 ◽  
Author(s):  
Mesrop Andriasyan ◽  
Juan Moyano ◽  
Juan Enrique Nieto-Julián ◽  
Daniel Antón
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Accuracy Assessment ◽  
Parametric Modelling ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Historical Interpretation ◽  
Information Modelling ◽  
Cloud Data ◽  
Building Information

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.

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.

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