scholarly journals EVALUATION OF ACQUISITION STRATEGIES FOR IMAGE-BASED CONSTRUCTION SITE MONITORING

2016 ◽  
Vol XLI-B5 ◽  
pp. 733-740 ◽  
Author(s):  
S. Tuttas ◽  
A. Braun ◽  
A. Borrmann ◽  
U. Stilla
Keyword(s):  
Point Clouds ◽  
Construction Site ◽  
Construction Sites ◽  
Site Monitoring ◽  
Acquisition Strategies ◽  
Stereo Reconstruction ◽  
Building Information ◽  
First Results ◽  
Aerial Vehicle ◽  
Construction Activity

Construction site monitoring is an essential task for keeping track of the ongoing construction work and providing up-to-date information for a Building Information Model (BIM). The BIM contains the as-planned states (geometry, schedule, costs, ...) of a construction project. For updating, the as-built state has to be acquired repeatedly and compared to the as-planned state. In the approach presented here, a 3D representation of the as-built state is calculated from photogrammetric images using multi-view stereo reconstruction. On construction sites one has to cope with several difficulties like security aspects, limited accessibility, occlusions or construction activity. Different acquisition strategies and techniques, namely (i) terrestrial acquisition with a hand-held camera, (ii) aerial acquisition using a Unmanned Aerial Vehicle (UAV) and (iii) acquisition using a fixed stereo camera pair at the boom of the crane, are tested on three test sites. They are assessed considering the special needs for the monitoring tasks and limitations on construction sites. The three scenarios are evaluated based on the ability of automation, the required effort for acquisition, the necessary equipment and its maintaining, disturbance of the construction works, and on the accuracy and completeness of the resulting point clouds. Based on the experiences during the test cases the following conclusions can be drawn: Terrestrial acquisition has the lowest requirements on the device setup but lacks on automation and coverage. The crane camera shows the lowest flexibility but the highest grade of automation. The UAV approach can provide the best coverage by combining nadir and oblique views, but can be limited by obstacles and security aspects. The accuracy of the point clouds is evaluated based on plane fitting of selected building parts. The RMS errors of the fitted parts range from 1 to a few cm for the UAV and the hand-held scenario. First results show that the crane camera approach has the potential to reach the same accuracy level.

scholarly journals EVALUATION OF ACQUISITION STRATEGIES FOR IMAGE-BASED CONSTRUCTION SITE MONITORING

2016 ◽  
Vol XLI-B5 ◽  
pp. 733-740 ◽  
Author(s):  
S. Tuttas ◽  
A. Braun ◽  
A. Borrmann ◽  
U. Stilla
Keyword(s):  
Point Clouds ◽  
Construction Site ◽  
Construction Sites ◽  
Site Monitoring ◽  
Acquisition Strategies ◽  
Stereo Reconstruction ◽  
Building Information ◽  
First Results ◽  
Aerial Vehicle ◽  
Construction Activity

Construction site monitoring is an essential task for keeping track of the ongoing construction work and providing up-to-date information for a Building Information Model (BIM). The BIM contains the as-planned states (geometry, schedule, costs, ...) of a construction project. For updating, the as-built state has to be acquired repeatedly and compared to the as-planned state. In the approach presented here, a 3D representation of the as-built state is calculated from photogrammetric images using multi-view stereo reconstruction. On construction sites one has to cope with several difficulties like security aspects, limited accessibility, occlusions or construction activity. Different acquisition strategies and techniques, namely (i) terrestrial acquisition with a hand-held camera, (ii) aerial acquisition using a Unmanned Aerial Vehicle (UAV) and (iii) acquisition using a fixed stereo camera pair at the boom of the crane, are tested on three test sites. They are assessed considering the special needs for the monitoring tasks and limitations on construction sites. The three scenarios are evaluated based on the ability of automation, the required effort for acquisition, the necessary equipment and its maintaining, disturbance of the construction works, and on the accuracy and completeness of the resulting point clouds. Based on the experiences during the test cases the following conclusions can be drawn: Terrestrial acquisition has the lowest requirements on the device setup but lacks on automation and coverage. The crane camera shows the lowest flexibility but the highest grade of automation. The UAV approach can provide the best coverage by combining nadir and oblique views, but can be limited by obstacles and security aspects. The accuracy of the point clouds is evaluated based on plane fitting of selected building parts. The RMS errors of the fitted parts range from 1 to a few cm for the UAV and the hand-held scenario. First results show that the crane camera approach has the potential to reach the same accuracy level.

scholarly journals ROBUST CLASSIFICATION AND SEGMENTATION OF PLANAR AND LINEAR FEATURES FOR CONSTRUCTION SITE PROGRESS MONITORING AND STRUCTURAL DIMENSION COMPLIANCE CONTROL

2015 ◽  
Vol II-3/W5 ◽  
pp. 129-136 ◽  
Author(s):  
R. Maalek ◽  
D. D. Lichti ◽  
J. Ruwanpura
Keyword(s):  
Moving Objects ◽  
Progress Monitoring ◽  
Point Clouds ◽  
Construction Site ◽  
Structural Dimension ◽  
Construction Sites ◽  
Linear Features ◽  
Robust Clustering ◽  
Robust Classification ◽  
Building Information

The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.

scholarly journals CLASSIFICATION OF PHOTOGRAMMETRIC POINT CLOUDS OF SCAFFOLDS FOR CONSTRUCTION SITE MONITORING USING SUBSPACE CLUSTERING AND PCA

2016 ◽  
Vol XLI-B3 ◽  
pp. 725-732 ◽  
Author(s):  
Y. Xu ◽  
S. Tuttas ◽  
L. Heogner ◽  
U. Stilla
Keyword(s):  
Classification Accuracy ◽  
Subspace Clustering ◽  
Point Clouds ◽  
Automatic Monitoring ◽  
Normal Vector ◽  
Construction Site ◽  
Building Information ◽  
Local Reference ◽  
Real Scenario

This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.

scholarly journals IMMERSIVE VISUALISATION OF CONSTRUCTION SITE POINT CLOUD DATA, MESHES AND BIM MODELS IN A VR ENVIRONMENT USING A GAMING ENGINE

2019 ◽  
Vol XLII-5/W2 ◽  
pp. 77-83 ◽  
Author(s):  
S. Vincke ◽  
R. de Lima Hernandez ◽  
M. Bassier ◽  
M. Vergauwen
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Partial Solution ◽  
Data Sources ◽  
Construction Site ◽  
Dimensional Approach ◽  
3D Design ◽  
Cloud Data ◽  
Current State ◽  
Building Information

<p><strong>Abstract.</strong> By adopting Building Information Modelling (BIM) software, the architecture, engineering and construction (AEC) industry shifted from a two-dimensional approach to a three-dimensional one in the design phase of a building. However, a similar three-dimensional approach for the visualisation of the current state of the construction works is lacking. Currently, progress reports typically include numerous pictures of the construction site or elements, alongside the appropriate parts of the 3D as-design BIM model. If a proper transition to a <i>3D design versus 3D current state</i> were achieved, the evolved type of reports would become more comprehensible, resulting in more well-informed decision-making. This requires a single, unique software platform that is able to import, process, analyse and visualise both the as-design BIM model as well as the recorded data of the current construction state. At present however, the visualisation and interpretation of the different datasets alone requires already multiple software packages.</p><p>As a partial solution this work presents a platform to easily visualise and interpret various data sources such as point clouds, meshes and BIM models and analysis results. Recent advances of gaming engines focus on and allow for an excellent visualisation of mesh data. Therefore all of the aforementioned data sources are converted into mesh objects upon importing. Moreover, gaming engines provide the necessary tools to traverse the scene intuitively allowing construction site managers and other stakeholders to gain a more complete and better oversight of the construction project. Furthermore, these engines also provide the possibility to take the immersion to the next level: incorporating the 3D entities into a Virtual Reality (VR) environment makes the visualised data and the executed analyses even more comprehensible.</p><p>By means of a case study, the potential of the presented approach is showcased. The real-world construction site recordings, models and analyses are visualised and implemented in VR using the Unity gaming engine.</p>

scholarly journals Adoption of environmental practices on construction sites

2017 ◽  
Vol 17 (4) ◽  
pp. 9-24 ◽  
Author(s):  
Natasha Ilse Rothbucher Thomas ◽  
Dayana Bastos Costa
Keyword(s):  
Environmental Impacts ◽  
Sustainable Management ◽  
Management Practices ◽  
Participant Observation ◽  
Economic Feasibility ◽  
Construction Site ◽  
Construction Sites ◽  
Environmental Practices ◽  
Construction Activity

Abstract Sustainability on construction sites and its impacts on the environment have become increasingly relevant. Large quantities of materials, water and energy, among other resources of various types and origins, are consumed on construction sites during the production activities and by the temporary facilities. This paper aims at presenting a set of criteria for the adoption of sustainable management practices on construction sites aiming at mitigating environmental impacts produced during the construction phase. An in-depth case study was carried out on the new building of the Construction Chamber for the state of Bahia, Brazil. Different environmental impacts for each construction activity specific to the construction site were studied and an economic feasibility study of each solution was performed, followed by the implementation of these practices. Monitoring data was collected through checklists, indicators and periodic project management meetings. Participant observation and document analysis were also used as sources of evidence. The main findings refer to the identification of solutions, main difficulties in adopting such solutions, the development of indicators for monitoring low environmental impact on construction sites, as well as a set of recommendations for the deployment of sustainable practices on construction sites.

scholarly journals IMAGE RECORDING CHALLENGES FOR PHOTOGRAMMETRIC CONSTRUCTION SITE MONITORING

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 747-753
Author(s):  
S. Vincke ◽  
M. Bassier ◽  
M. Vergauwen
Keyword(s):  
Remote Sensing ◽  
Progress Monitoring ◽  
External Factors ◽  
Construction Site ◽  
Construction Sites ◽  
Auxiliary Equipment ◽  
Image Recording ◽  
Site Monitoring ◽  
Remote Sensing Techniques ◽  
Specific Challenge

<p><strong>Abstract.</strong> Construction site monitoring and progress monitoring is becoming increasingly popular in the architecture, engineering and construction (AEC) industry. To this end remote sensing techniques are used to gather consecutive datasets of the construction site. This work focuses on the recording of imagery for photogrammetric processing and the challenging conditions often encountered on construction sites. The constantly evolving character of a such sites requires datasets to be captured as quickly as possible. Furthermore other recording complexities arise such as the presence of auxiliary equipment and clutter or reflections caused by wet surfaces, hindering quick and complete recordings. Apart from these external factors also construction elements themselves often complicate the capturing workflow.</p><p>This work enumerates several real-world examples of difficulties construction sites pose for the recording of imagery for photogrammetry purposes. Each section provides an insight in a specific challenge, typical for construction sites, and discusses applicable field-tested solutions including an overview of relevant solutions found in literature.</p>

scholarly journals BIM-based Hazard Recognition and Evaluation Methodology for Automating Construction Site Risk Assessment

2020 ◽  
Vol 10 (7) ◽  
pp. 2335 ◽  
Author(s):  
Inhan Kim ◽  
Yongha Lee ◽  
Jungsik Choi
Keyword(s):  
Risk Assessment ◽  
Subjective Experience ◽  
Safety Management ◽  
Information Modeling ◽  
Evaluation Methodology ◽  
Construction Site ◽  
Construction Sites ◽  
Work Related ◽  
Risk Rating ◽  
Building Information

Construction sites in Korea are the locus of many disasters and work-related illnesses, and construction workers are particularly likely to be exposed to serious disasters such as falls, collapses, and burial. At domestic construction sites, the concept of Design for Safety has been adopted from abroad, and current regulations are intended to secure the personnel safety at each site. However, current government guidelines and regulations are difficult to apply in the field, mainly because they do not clearly address work issues and safety management measures. The current safety review method depends too much on the subjective experience of site workers or managers. This study analyzes the step-by-step tasks required for more automated building information modeling (BIM)-based construction site safety management. An example BIM-based assessment of one specific construction site hazard, the risk of a worker fall, is carried out. In the risk analysis stage, all of the associated hazards are identified and the attendant risks are estimated and quantified. A broader risk rating methodology is derived based on the scenarios of each possible disaster at a construction site, and the hazards are extracted by defining the relationships between each building element based on the BIM information. The result is a risk rating methodology derived from a BIM-based risk assessment.

scholarly journals CLASSIFICATION OF PHOTOGRAMMETRIC POINT CLOUDS OF SCAFFOLDS FOR CONSTRUCTION SITE MONITORING USING SUBSPACE CLUSTERING AND PCA

2016 ◽  
Vol XLI-B3 ◽  
pp. 725-732 ◽  
Author(s):  
Y. Xu ◽  
S. Tuttas ◽  
L. Heogner ◽  
U. Stilla
Keyword(s):  
Classification Accuracy ◽  
Subspace Clustering ◽  
Point Clouds ◽  
Automatic Monitoring ◽  
Normal Vector ◽  
Construction Site ◽  
Building Information ◽  
Local Reference ◽  
Real Scenario

This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.

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.

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