scholarly journals Comparision of photogrammetric point clouds with BIM building elements for construction progress monitoring

2014 ◽  
Vol XL-3 ◽  
pp. 341-345 ◽  
Author(s):  
S. Tuttas ◽  
A. Braun ◽  
A. Borrmann ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
Progress Monitoring ◽  
Point Clouds ◽  
Actual State ◽  
Existing Building ◽  
Building Information Model ◽  
Building Information ◽  
City Construction ◽  
Construction Progress Monitoring ◽  
Construction Schedule

For construction progress monitoring a planned state of the construction at a certain time (as-planed) has to be compared to the actual state (as-built). The as-planed state is derived from a building information model (BIM), which contains the geometry of the building and the construction schedule. In this paper we introduce an approach for the generation of an as-built point cloud by photogrammetry. It is regarded that that images on a construction cannot be taken from everywhere it seems to be necessary. Because of this we use a combination of structure from motion process together with control points to create a scaled point cloud in a consistent coordinate system. Subsequently this point cloud is used for an as-built – as-planed comparison. For that voxels of an octree are marked as occupied, free or unknown by raycasting based on the triangulated points and the camera positions. This allows to identify not existing building parts. For the verification of the existence of building parts a second test based on the points in front and behind the as-planed model planes is performed. The proposed procedure is tested based on an inner city construction site under real conditions.

scholarly journals Automated Progress Controlling and Monitoring Using Daily Site Images and Building Information Modelling

Buildings ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 70 ◽  
Author(s):  
Hadi Mahami ◽  
Farnad Nasirzadeh ◽  
Ali Hosseininaveh Ahmadabadian ◽  
Saeid Nahavandi
Keyword(s):  
Point Cloud ◽  
Construction Projects ◽  
Progress Monitoring ◽  
Point Clouds ◽  
3D Point Cloud ◽  
Project Duration ◽  
3D Point Clouds ◽  
Cloud Models ◽  
Building Information ◽  
Construction Progress Monitoring

This research presents a novel method for automated construction progress monitoring. Using the proposed method, an accurate and complete 3D point cloud is generated for automatic outdoor and indoor progress monitoring throughout the project duration. In this method, Structured-from-Motion (SFM) and Multi-View-Stereo (MVS) algorithms coupled with photogrammetric principles for the coded targets’ detection are exploited to generate as-built 3D point clouds. The coded targets are utilized to automatically resolve the scale and increase the accuracy of the point cloud generated using SFM and MVS methods. Having generated the point cloud, the CAD model is generated from the as-built point cloud and compared with the as-planned model. Finally, the quantity of the performed work is determined in two real case study projects. The proposed method is compared to the Structured-from-Motion (SFM)/Clustering Multi-Views Stereo (CMVS)/Patch-based Multi-View Stereo (PMVS) algorithm, as a common method for generating 3D point cloud models. The proposed photogrammetric Multi-View Stereo method reveals an accuracy of around 99 percent and the generated noises are less compared to the SFM/CMVS/PMVS algorithm. It is observed that the proposed method has extensively improved the accuracy of generated points cloud compared to the SFM/CMVS/PMVS algorithm. It is believed that the proposed method may present a novel and robust tool for automated progress monitoring in construction projects.

scholarly journals VALIDATION OF BIM COMPONENTS BY PHOTOGRAMMETRIC POINT CLOUDS FOR CONSTRUCTION SITE MONITORING

2015 ◽  
Vol II-3/W4 ◽  
pp. 231-237 ◽  
Author(s):  
S. Tuttas ◽  
A. Braun ◽  
A. Borrmann ◽  
U. Stilla
Keyword(s):  
Progress Monitoring ◽  
Point Clouds ◽  
Building Information Model ◽  
Building Model ◽  
Site Monitoring ◽  
Building Information ◽  
Local Plane ◽  
Raster Cell ◽  
Construction Progress Monitoring ◽  
Processing Steps

Construction progress monitoring is a primarily manual and time consuming process which is usually based on 2D plans and therefore has a need for an increased automation. In this paper an approach is introduced for comparing a planned state of a building (as-planned) derived from a Building Information Model (BIM) to a photogrammetric point cloud (as-built). In order to accomplish the comparison a triangle-based representation of the building model is used. The approach has two main processing steps. First, visibility checks are performed to determine whether or not elements of the building are potentially built. The remaining parts can be either categorized as free areas, which are definitely not built, or as unknown areas, which are not visible. In the second step it is determined if the potentially built parts can be confirmed by the surrounding points. This process begins by splitting each triangle into small raster cells. For each raster cell a measure is calculated using three criteria: the mean distance of the points, their standard deviation and the deviation from a local plane fit. A triangle is confirmed if a sufficient number of raster cells yield a high rating by the measure. The approach is tested based on a real case inner city scenario. Only triangles showing unambiguous results are labeled with their statuses, because it is intended to use these results to infer additional statements based on dependencies modeled in the BIM. It is shown that the label built is reliable and can be used for further analysis. As a drawback this comes with a high percentage of ambiguously classified elements, for which the acquired data is not sufficient (in terms of coverage and/or accuracy) for validation.

Mapping Unmanned Aerial System Data onto Building Information Modeling Parameters for Highway Construction Progress Monitoring

2021 ◽  
pp. 036119812110642
Author(s):  
Reihaneh Samsami ◽  
Amlan Mukherjee ◽  
Colin N. Brooks
Keyword(s):  
Project Management ◽  
Progress Monitoring ◽  
Building Information Modeling ◽  
Highway Construction ◽  
Point Clouds ◽  
Project Managers ◽  
Information Modeling ◽  
Unmanned Aerial System ◽  
Building Information ◽  
Construction Progress Monitoring

The transportation infrastructure management sector lacks automated procedures that can help it find and resolve the performance deviations. The objective of this research is to illustrate the mapping of Unmanned Aerial System (UAS) collected photogrammetric data to building information modeling (BIM) parameters, and their application for automated construction progress monitoring and the generation of as-built models. The goal is to support project managers to estimate project progress during highway construction. As a part of ongoing work, this paper takes into account 4D (3D + time) data that is acquired from 3D surface digital elevation models, point clouds, LiDAR data, and orthographic photos. It maps these 4D data onto BIM parameters to create as-built models of the project at different intervals. A comparison between as-planned and as-built models using the earned value management method is employed to develop metrics that can be used for indicating cost and schedule deviations during construction. The mapping methodology introduced in this paper is illustrated using an ongoing highway construction project case study. The main contribution of this paper is the organization, processing, and integration of UAS data with BIM data structures and project management workflows. The research outcomes will assist project managers in an easy and quick identification of potential performance problems and support the project management decision-making process.

scholarly journals ENERGY FUNCTION ALGORITHM FOR DETECTION OF OPENINGS IN INDOOR POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 747-752
Author(s):  
R. Assi ◽  
T. Landes ◽  
H. Macher ◽  
P. Grussenmeyer
Keyword(s):  
Energy Function ◽  
Point Cloud ◽  
Point Clouds ◽  
Architectural Heritage ◽  
Building Information Model ◽  
Binary Images ◽  
Manual Intervention ◽  
Building Information ◽  
Modelling Process ◽  
Data Term

<p><strong>Abstract.</strong> As the use of building information model (BIM) for architectural heritage becomes more relevant, this paper explores different solutions to further automatize the modelling process. The scan-to-BIM process still requires manual intervention that is time consuming, subject to errors and user-dependent. In this paper, the main focus is the automated segmentation of windows. In the first part of our paper, we will review and compare several state-of-the-art methods for automatic detection and segmentation of openings in a point cloud. Based on the most pertinent aspects of those methods, a new algorithm focusing on indoor point clouds is proposed. After walls are already detected, they are converted in 2D binary images. Holes in those images correspond to openings. We submit each opening to an energy function with two terms: data and coherence. The data term depends on the shape of the opening. The coherence term considers the position of the opening in the scene. Those function let us determine if an opening in the point cloud is due to a window/door or an object obstructing the acquisition. In the third part we discuss the results obtained by applying the method to different datasets.</p>

scholarly journals Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring

2021 ◽  
Vol 11 (17) ◽  
pp. 7840
Author(s):  
Jingguo Xue ◽  
Xueliang Hou ◽  
Ying Zeng
Keyword(s):  
3D Reconstruction ◽  
Progress Monitoring ◽  
Three Dimensional ◽  
Future Research ◽  
Project Schedule ◽  
Construction Sites ◽  
Building Information Model ◽  
Building Information ◽  
Future Research Directions ◽  
Construction Progress Monitoring

With the spread of camera-equipped devices, massive images and videos are recorded on construction sites daily, and the ever-increasing volume of digital images has inspired scholars to visually capture the actual status of construction sites from them. Three-dimensional (3D) reconstruction is the key to connecting the Building Information Model and the project schedule to daily construction images, which enables managers to compare as-planned with as-built status and detect deviations and therefore monitor project progress. Many scholars have carried out extensive research and produced a variety of intricate methods. However, few studies comprehensively summarize the existing technologies and introduce the homogeneity and differences of these technologies. Researchers cannot clearly identify the relationship between various methods to solve the difficulties. Therefore, this paper focuses on the general technical path of various methods and sorts out a comprehensive research map, to provide reference for researchers in the selection of research methods and paths. This is followed by identifying gaps in knowledge and highlighting future research directions. Finally, key findings are summarized.

scholarly journals Automated Verification of Building Components Using BIM Models and Point Clouds

2020 ◽  
Vol 28 (3) ◽  
pp. 13-19
Author(s):  
Richard Honti ◽  
Ján Erdélyi ◽  
Gabriela Bariczová ◽  
Tomáš Funtík ◽  
Pavol Mayer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Automated Verification ◽  
Building Information Model ◽  
Industry Foundation Classes ◽  
Exchange Format ◽  
Building Information ◽  
Building Components

AbstractOne of the most important parts of construction work is the verification of the geometry of the parts of structures and buildings constructed. Today this procedure is often semi- or fully automated. The paper introduces an approach for the automated verification of parts of buildings, by comparing the design of a building (as-planned model), derived from a Building Information Model (BIM) in an Industry Foundation Classes (IFC) exchange format to a terrestrial laser scanning (TLS) point cloud (as-built model). The approach proposed has three main steps. The process begins with the acquisition of information from the as-planned model in the IFC exchange format; the second step is the automated (wall) plane segmentation from the point cloud. In the last step, the two models mentioned are compared to determine the deviations from the design, and the as-built wall flatness quantification is also executed. The potential of the proposed algorithm is shown in a case-study.

Integration of Industry 4.0 for Advanced Construction Project Management

2021 ◽  
pp. 1277-1311
Author(s):  
Nataša Šuman ◽  
Zoran Pučko
Keyword(s):  
Project Management ◽  
Industry 4.0 ◽  
Progress Monitoring ◽  
Construction Project ◽  
Information Modeling ◽  
Construction Project Management ◽  
Building Information ◽  
Project Participants ◽  
Construction Progress Monitoring ◽  
Project Lifecycle

The construction industry is facing the increasing process of integration of Industry 4.0 in all phases of the construction project lifecycle. Its exponential growth has been detected in research efforts focused on the usage of the building information modeling (BIM) as one of the most breakthrough innovative approaches in the construction (AEC) industry. BIM brings many advantages as well as changes in the existing construction practice, which allows for adjustment of processes in the most automated possible way. The goal in the design phase is to create a comprehensive BIM model that combines the data of all project participants and represents a digital model of a future building. In the construction phase, the monitoring and controlling the work progress is one of the most important and difficult tasks, and it is today still mostly done manually. Currently, more research and actual implementations are oriented towards the introduction of the automated construction progress monitoring (ACPMon). All of this is the basis for advanced construction project management (ACPMan).

scholarly journals An Efficient Pipeline to Obtain 3D Model for HBIM and Structural Analysis Purposes from 3D Point Clouds

2020 ◽  
Vol 10 (4) ◽  
pp. 1235 ◽  
Author(s):  
Massimiliano Pepe ◽  
Domenica Costantino ◽  
Alfredo Restuccia Garofalo
Keyword(s):  
Structural Analysis ◽  
Case Studies ◽  
Point Cloud ◽  
3D Model ◽  
Point Clouds ◽  
Single Element ◽  
Building Information Modelling ◽  
Information Modelling ◽  
3D Point Clouds ◽  
Building Information

The aim of this work is to identify an efficient pipeline in order to build HBIM (heritage building information modelling) and create digital models to be used in structural analysis. To build accurate 3D models it is first necessary to perform a geomatics survey. This means performing a survey with active or passive sensors and, subsequently, accomplishing adequate post-processing of the data. In this way, it is possible to obtain a 3D point cloud of the structure under investigation. The next step, known as “scan-to-BIM (building information modelling)”, has led to the creation of an appropriate methodology that involved the use of Rhinoceros software and a few tools developed within this environment. Once the 3D model is obtained, the last step is the implementation of the structure in FEM (finite element method) and/or in HBIM software. In this paper, two case studies involving structures belonging to the cultural heritage (CH) environment are analysed: a historical church and a masonry bridge. In particular, for both case studies, the different phases were described involving the construction of the point cloud and, subsequently, the construction of a 3D model. This model is suitable both for structural analysis and for the parameterization of rheological and geometric information of each single element of the structure.

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 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.

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