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A Framework for Automatic Tolerance Analysis of Removable Flood Wall Anchor Plates With 3D Laser Scanning and BIM

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Integrating and storing digital data through Building Information Modeling (BIM) and 3D Laser Scanning is of most importance in terms of visualizing project information. In this respect, this study presents a systematic and practical approach for detecting the assembly quality of anchor plates for a removable floodwall through the integration of BIM and 3D laser scanning. The current methods for analyzing the assembly quality of anchor plates heavily rely on manual inspection and contact-type measurements, which are time-consuming and costly. Therefore, this paper examines a semi-automated method integrating the use of BIM and 3D laser scanning technology for rapid analysis of the assembly quality of anchor plates. In this context, the paper introduces the framework of an automatic dimensional and surface quality assessment method. The following sections describe the project flowchart, data collection, and quality inspection methodology. The study employs the data of a real project located in Heihe, China, to validate the level of technical feasibility and accuracy of the presented methods. The results indicated that the proposed integration of BIM and 3D laser scanning has the potential to produce a semiautomated and reliable method to control the assembly quality of anchor plates.
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6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
A Framework for Automatic Tolerance Analysis of
Removable Flood Wall Anchor Plates With 3D Laser Scanning
and BIM
H. Li
North China University of Water Conservancy and Electric Power, Department of
Construction Engineering and Management, China
lihuimin3646@163.com
C. Zhang
University of Wyoming, Department of Civil and Architectural Engineering, USA
chengyi.zhang@uwyo.edu
S. Song
University of Southern Mississippi, School of Construction and Design, USA
siyuan.Song@usm.edu
S. Demirkesen
Gebze Technical University, Department of Civil Engineering, Turkey
demirkesen@gtu.edu.tr
Abstract
Integrating and storing digital data through Building Information Modeling (BIM) and
3D Laser Scanning is of most importance in terms of visualizing project information. In
this respect, this study presents a systematic and practical approach for detecting the
assembly quality of anchor plates for a removable floodwall through the integration of
BIM and 3D laser scanning. The current methods for analyzing the assembly quality of
anchor plates heavily rely on manual inspection and contact-type measurements, which are
time-consuming and costly. Therefore, this paper examines a semi-automated method
integrating the use of BIM and 3D laser scanning technology for rapid analysis of the
assembly quality of anchor plates. In this context, the paper introduces the framework of an
automatic dimensional and surface quality assessment method. The following sections
describe the project flowchart, data collection, and quality inspection methodology. The
study employs the data of a real project located in Heihe, China, to validate the level of
technical feasibility and accuracy of the presented methods. The results indicated that the
proposed integration of BIM and 3D laser scanning has the potential to produce a semi-
automated and reliable method to control the assembly quality of anchor plates.
Keywords: removable flood wall, laser scanning, BIM, assembly quality analysis.
56
Introduction
A floodwall is a primarily vertical artificial barrier that is erected along the banks of a stream
or path of floodwaters to prevent floodwaters from reaching the area behind the structure
during seasonal or extreme weather events. Traditional floodwalls are mainly constructed
from prefabricated concrete elements. This system requires considerable land area, which may
restrict access to the structure, and can be very expensive on construction and maintenance.
Hence, a removable floodwall system is designed and introduced to overcome those
challenges brought by the traditional floodwalls. Unlike traditional ones, these structures are
movable and can be deployed or erected only in times of flooding. They are typically lower in
cost than levees or traditional floodwalls. Removable floodwall systems can be applied to
individual buildings, smaller areas, or a large scale of the infrastructure such as dams, large-
size port piers, railway tunnel portals, culvert openings of expressways, openings of civil air
defense structures, and urban large-scale communities to prevent flood disasters (Chen et al.,
2018). Compared with traditional flood protection methods, the removable floodwall takes
advantage of the low labor intensity, high work efficiency, and small seepage (Kádár, 2015).
Also, a removable floodwall gives protection in case of flooding and open access to the
floodplain over the remaining time. It can also be used as an emergency tool against flooding
in unprotected low-lying areas in addition to the heightening of permanent flood protection
structures in extreme events.
One of the critical procedures during the installation process is the accuracy tolerance control
of anchor plates, which are embedded in reinforced concrete plinths for connecting fixed
columns. Ensuring the installation precision of anchor plates without deviation during
concrete pouring is crucial because the control precision of concrete pouring is in millimeter
accuracy. The current methods for analyzing the tolerance accuracy of anchor plates, however,
rely primarily on manual inspections and contact type measurements such as rulers and
measurement tapes, which are time consuming and costly. Also, there is a lack of systematic
storage and management of the information obtained. A major portion of the researchers and
practitioners adopt non-contact sensing techniques to monitor the dimensional properties of
the precast structures. Laser scanners have recently been one of the most popular recent
measurement tools in the construction industry (Kim et al., 2015). Laser scanning directly
acquires 3D point cloud data at a high accuracy level with 2mm to 6mm at 50 meters
(Muszyński & Rybak, 2017; Olsen et al., 2010). According to the existing studies (Dai & Lu,
2010; Dai et al., 2013; Golparvar-Fard et al., 2011), which conducted comparisons between
laser scanning methods and vision based approaches, the laser scanning approaches offer
better accuracy than vision based methods.
This paper introduces a semi-automated method that integrates the use of BIM and 3D laser
scanning technology to enable rapid analysis of the assemble quality of anchor plates for a
removable floodwall. The researchers first propose a framework of an automatic dimensional
and surface quality assessment method. The following sections describe the data capture,
quality inspection procedure, and the data storage and delivery methods accordingly. The
study utilizes the data of a real project located in Heihe, China for validating the level of
technical feasibility and accuracy of the presented methods. The results indicate that the
proposed integration of BIM and 3D laser scanning has the potential to produce a semi-
automated and reliable method to proactively control the assemble quality of anchor plates for
removable floodwall during field installations.
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
57
Literature Review
BIM for Construction Quality Control
BIM is an effective tool in terms of collaborative planning and team working. It has several
modules such as cost, time, quality, and safety, which enable a construction project to be
executed with higher performance. It is also a beneficiary tool for quality control. BIM is a
rich and formal model that provides an ample number of possibilities for automated quality
inspections; it can interpret and execute a variety of criteria ranging from client requirements
to health codes, safety codes, and building design and construction regulations (Park & Kim,
2015). There are three types of BIM-based quality control methods (Choi, 2012), namely the
physical quality control, logical quality control, and data quality control. Physical quality
control includes elements checking, and clash checking between physical elements on
different construction sections such as MEP and structure. Logical quality control relies on
rule-based checking using formulas, architecture acts, guidelines, specifications, and so on.
Data quality control refers to data reliability checking and it checks whether a specific
component has its proper attributes or not (Park & Kim, 2015). Kim et al. (2013) developed a
BIM evacuation simulation module, called InSightBIM™, for BIM-based quality checking in
supertall buildings. Zhang et al. (2013) developed an automated error detection module and an
installation system (i.e., fall protection installations such as staircases, slab edges, slab
openings, and protective equipment) operated through Tekla™.
Laser Scanning for Construction Quality Control
Laser Scanning is a novel surveying technology. A laser scanner sweeps the surrounding space
with laser light to acquire 3D data points with reasonable accuracy, great speed, and high
density. Point clouds provided by laser scanners can be used directly for measurement and
visualization (Bosché & Guenet, 2014). One of the important applications of laser scanning is
construction quality control (Akinci et al., 2006; Boukamp & Akinci, 2007; Tang et al., 2011).
Akinci et al. (2006) proposed a first formalization for integrating project 3D models and
sensor systems to defect detection and characterization for construction quality control.
Bosché and Guenet (2014) then presented the implementation of such a system, called the
Scan-vs-BIM principle. According to the different types of geometry quality, research efforts
on geometry quality inspection can be divided into three categories, namely (1) dimensional
quality inspection, (2) surface quality inspection, and (3) displacement inspection (Wang &
Kim, 2019). The dimensional quality inspection has covered dimensions of prefabricated
elements and dimensions of building façade elements, such as size (Kim et al., 2014; Wang et
al., 2016), shape (Kim et al., 2016), position (Bosché, 2010), and orientation (Bosché, 2010).
For surface quality inspection, most research works are focused on surface crack, spalling,
flatness, and deformation/distortion. Displacement inspection is focused on the change of
relative position of a structure or elements.
Integrated BIM and Laser Scanning for Construction Quality Control
The integration of 3D laser scanning and BIM offers an opportunity for construction quality
control (Wang et al., 2017). During the construction phase, the change orders always occur.
Therefore, construction works on site need to be assessed to make sure the as-built
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
58
construction outcomes are consistent with the as-designed BIM model. Moreover, the
discrepancy should be checked if it is less than the tolerance value (Wang & Kim, 2019).
Tang and Akinci (2012) utilized laser scanners to collect dense 3D point clouds for bridge
inspectors. Akinci et al. (2006) utilized sensing technologies and project modeling capabilities
to develop an active quality control system, which included a process of acquiring and
updating detailed design information, identifying inspection goals, inspection planning, as-
built data acquisition and analysis, and defect detection and management. Bosché (2010)
introduced an approach for automated recognition of project 3D BIM objects in large laser
scans to automatically control the compliance of projects with respect to corresponding
dimensional tolerances.
Methodology
This study analyzed anchor plates for removable floodwall using 3D laser scanning and BIM
technology for quality controls. A research framework was developed, as shown in Figure 1.
The research was conducted in eight steps.
Figure 1: Research framework.
Step 1: Literature review. A thorough literature review and background study were conducted
to evaluate the feasibility of using 3D laser scanning and BIM for tolerance analysis.
Step 2: A BIM model was created based on the design and the assembly instructions of the
anchor plates.
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
59
Step 3: Laser scanning and noise removal. This work included the selection of a suitable laser
scanner, optimization of scanning methods, parameters, and locations, and removing point
cloud noise.
Step 4: Scan to BIM. The point cloud data was converted into 3D BIM in Revit.
Step 5: Semi-automated method development. Integration of the use of BIM and 3D laser
scanning technology to enable rapid analysis of the assemble quality of anchor plates for
removable floodwall.
Step 6: Laboratory validation. An indoor test was conducted to validate the laser scanner
accuracy and optimize the selections of the laser scanning parameters. The field test will
continue after passing the indoor testing.
Step 7: Field implementation. The author performed quality control and quality assurance for
installing the anchor plates and then compares it with the manual installation quality test
report.
Step 8: Semi-automatic optimization. Field data was analyzed to find out the deficiencies in
the program and to summarize and improve the integrated installation quality assessment for
the anchor plate.
Case Study
This study implemented the methodology in a real-life project. The project is along with
Heilongjiang river, within the city limits of Heihe, China. The length of the removable
floodwall is 3,135 meters, with a total of 1,056 prefabricated anchor plates. The purpose of
installing a removable floodwall is to meet the new flood control standard without blocking
the view of the skyline. Anchor plates are identical to each other with the same installation
procedure of each anchor plate. The authors decide to use two sample anchor plates to
describe how the quality analysis was performed in this study.
Developing a BIM Model
The floodwall anchor plates used in this study were produced by the IBS company of
Germany. A 3D model of the anchor plate was developed, as shown in Figure 2a. The
individual component of the anchor plate is shown in Figure 2b.
Figure 2a: Anchor plate – BIM Model.
Figure 2b: Anchor plate – Components.
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
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Based on the design and construction drawing, the research team developed three-dimensional
BIM models in Revit and georeferenced it to the coordination system used by the project. BIM
technology has the potentials to manage the life cycle of removable floodwall projects and
improve project quality and efficiency, reduce project cost, reworks, and wastes. Figure 3a and
Figure 3b present the BIM model of the reinforcement structure and the overall structure after
pouring concrete, respectively.
Figure 3a: Installation BIM model before pouring
concrete.
Figure 3b: Installation BIM
model after pouring concrete.
Capture of Point Cloud Data
The laser scanner used in the project was determined by factors such as the range and
accuracy specifications of the scanner, the accuracy requirements of the project, the cost of the
scanner, as well as the budget of the entire project. The location and the scanning parameters
of the scan were crucial to maintain a high level of accuracy. There are three key impact
factors of the scanning accuracy: (1) distance to the object; (2) incident angle between the 3D
laser scan and the installed anchor plates; (3) angular resolution of the 3D laser scanner. After
all those factors are studied, the Leica P30 laser scanner was selected to capture the data.
Multiple scans were then performed to minimize the occlusion effect.
Laser scans the anchor plates before pouring concrete: The anchor plates were installed in a
reinforced concrete foundation and embedded flush with the ground. The plate accepted the
center posts. In non-operative mode, the anchor plates’ internal threaded bushings are closed
with dummy bolts. They protect the bushings from debris. In case of a flood, the dummy bolts
are removed, and service bolts are utilized to connect the center posts with the anchor plates.
Figure 4 shows the anchor plates, which were installed every 30 meters according to the plan
and specification.
Figure 4: Anchor plate positions.
After an anchor plate was placed and levelled, it was permanently welded to the reinforcement
to protect any movement during the cast-in-place process, as shown in Figure 5a. Multiple
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
61
scans were performed to capture the point cloud data. Control targets were placed at the
recommended optimal distance from the scanner and evenly throughout the scan at different
elevations. Then, point clouds were tied into a single coordinate system, as shown in Figure
5b.
Figure 5a: Anchor plate in the
jobsite.
Figure 5b: Anchor plate point cloud.
Laser scans the anchor plates after pouring concrete: When the correct positions of anchor
plates were checked and documented, concrete work was then performed. After the concrete
was cured, scans were conducted to capture the as-built position of the anchor plate, as shown
in Figure 6a and Figure 6b. The data were compared to the point cloud before pouring
concrete to check if the position was changed due to the concrete work.
Data Analysis and Result
After performing a 3D laser scan, the raw point cloud data need to be processed. Cyclone was
selected as the software to process point cloud data because it is a software developed by
Leica, the manufacturer of 3D laser scanner used in the study, hence avoiding compatibility
problems in between the software and the equipment, and avoiding the cost of purchasing a
new software since Cyclone comes with the Leica 3D laser scanner.
Figure 6a: Scan anchor plate
after pouring concrete.
Figure 6b: Anchor plate point cloud after pouring concrete.
The project used China Geodetic Coordinate System 2000 (CGCS2000) as its main
georeference system. Each control point was surveyed by the Lecia Total Station under the
CGCS2000 system. When the point clouds were tied into the control points, the entire point
cloud is georeferenced in the CGCS2000 system. Thus, the BIM model and point cloud data
were in the same coordinate system and could be compared. Due to the high density of the
original point cloud data, it was necessary to eliminate the point cloud noise and use the
feature extraction algorithm to reduce the number of point clouds and the reading time of the
computer. Then, the point cloud data were extracted and saved in IFC format for storage, to
facilitate comparison with BIM data.
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
62
Figure 8: Design tolerance.
Based on the design tolerance provided by the manufacturer, the offset on the X-axis (+/-
5mm), Y-axis (+/- 3mm), and Z-axis (+/- 10mm) need to be evaluated and measured, as
shown in Figure 8. In order to measure the torsion deviation, each anchor plate was controlled
by four checkpoints, which are the four corners. By comparing the created BIM model and the
scanned point cloud data, the errors were calculated automatically, as shown in Figure 9.
Figure 9. Comparison between BIM and point cloud data.
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
63
Distance
Meet the quality requirement
Point-Point
3003 mm
Yes
X distance
-3 mm
Yes
Y distance
3 mm
Yes
Z distance
2 mm
Yes
Table 2. Deviation of the control points.
Anchor
Plat
Control
Point
Error(mm)
X
Y
Z
Left
A
2
1
3
B 3 3 5
C 2 3 2
D
2
3
3
Right
E 2 3 4
F
1
2
3
G 2 2 2
H
3
1
5
Conclusion
This study built a framework of integration of BIM and 3D laser scanning technology to
control the construction quality of removable floodwall anchor plates. BIM technology was
used to establish an accurate three-dimensional model according to the design. Meanwhile, 3D
laser scanning technology was used to scan anchor plates before pouring concrete and after.
Based on the tolerance requirements, a comparison was performed between the point cloud
and BIM model to determine whether the tolerance was within the allowable range. A series
of rectifying measures were carried out for the components beyond the allowable range of
tolerance to ensure the quality of the construction and installation. It did not only improved
the efficiency of construction quality control but also established a preliminary foundation for
semi-automation of quality control for built-in fitting in the concrete structure. It also provided
a reference for the combined application of BIM and 3D Laser in future scientific research.
As a future work, a robust system will be developed to automatically extract the key points
such as the center point, corners, and alignment of the embedded parts for further comparative
analysis. The combination of BIM and 3D laser scanning in the whole process will
significantly reduce the time consumption of manual measurement and avoid the error of
manual measurement. It can sufficiently improve efficiency while ensuring the precision of
Researchers examined the distance and the offset of the three axes between the center point of
the anchor plate. The required center to center distance is 3000mm. The actual distance is
3003 mm with -3mm on X-axis, 3mm on Y-axis, and 2mm on Z-axis. All of them met
the quality requirement, as shown in Table 1. Table 2 shows the deviation of the control
points to determine the torsion quality. The most significant deviation is 5mm on Z-axis,
and it is within the requirement of +/- 10 mm.
Table 1. Deviation from center point to center point.
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Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
64
quality control, and finally achieve comprehensive automatic quality control. How to build an
automated algorithm to get more accurate dimensions of anchor plates is the further research
direction.
References
Akinci, B., Boukamp, F., Gordon, C., Huber, D., Lyons, C., & Park, K. (2006). A formalism
for utilization of sensor systems and integrated project models for active construction quality
control. Automation in Construction, 15(2), 124-138.
Bosché, F. (2010). Automated recognition of 3D CAD model objects in laser scans and
calculation of as-built dimensions for dimensional compliance control in
construction. Advanced Engineering Informatics, 24(1), 107-118.
Bosché, F., & Guenet, E. (2014). Automating surface flatness control using terrestrial laser
scanning and building information models. Automation in Construction, 44, 212-226.
Boukamp, F., & Akinci, B. (2007). Automated processing of construction specifications to
support inspection and quality control. Automation in Construction, 17(1), 90-106.
Chen, S., Li, H., Guo, L., Wang, L., & Cao, Y. (2018). Testing the key performance of mobile
flood protection system. Advances in Civil Engineering, 2018, 1-11.
doi:10.1155/2018/5641385.
Choi, J. (2012). openBIM and Design Information Quality Control. Goomi Books Inc., Korea.
Čuš-Babič, N., Rebolj, D., Nekrep-Perc, M., & Podbreznik, P. (2014). Supply-chain
transparency within industrialized construction projects. Computers in Industry, 65(2), 345-
353.
Dai, F., & Lu, M. (2010). Assessing the accuracy of applying photogrammetry to take
geometric measurements on building products. Journal of Construction
Engineering and Management, 136(2), 242-250. doi:doi:10.1061/(ASCE)CO.1943-7862.000
0114 .
Dai, F., Rashidi, A., Brilakis, I., & Vela, P. (2013). Comparison of image-based and time-of-
flight-based technologies for three-dimensional reconstruction of infrastructure. Journal of
Construction Engineering and Management, 139(1), 69-79.
Golparvar-Fard, M., Bohn, J., Teizer, J., Savarese, S., & Peña-Mora, F. (2011). Evaluation of
image-based modeling and laser scanning accuracy for emerging automated performance
monitoring techniques. Automation in Construction, 20(8), 1143-1155.
Kádár, I. (2015). Mobile flood protection walls. Pollack Periodica, 10(1), 133-142.
doi:10.1556/Pollack.10.2015.1.13.
6th International Project and Construction Management Conference (e-IPCMC2020)
Istanbul Technical University, 12-14 November 2020, Istanbul, Turkey
65
Kim, M.-K., Cheng, J. C. P., Sohn, H., & Chang, C.-C. (2015). A framework for dimensional
and surface quality assessment of precast concrete elements using BIM and 3D laser
scanning. Automation in Construction, 49, 225-238.
Kim, M.-K., Sohn, H., & Chang, C.-C. (2014). Automated dimensional quality assessment of
precast concrete panels using terrestrial laser scanning. Automation in Construction, 45, 163-
177.
Kim, M.-K., Wang, Q., Park, J.-W., Cheng, J. C., Sohn, H., & Chang, C.-C. (2016).
Automated dimensional quality assurance of full-scale precast concrete elements using laser
scanning and BIM. Automation in Construction, 72, 102-114.
Muszyński, Z., & Rybak, J. (2017). Evaluation of terrestrial laser scanner accuracy in the
control of hydrotechnical structures. Studia Geotechnica et Mechanica, 39(4), 45-57.
doi:10.1515/sgem-2017-0036
Olsen, M. J., Kuester, F., Chang, B. J., & Hutchinson, T. C. (2010). Terrestrial laser scanning-
based structural damage assessment. Journal of Computing in Civil Engineering, 24(3), 264-
272.
Park, S., & Kim, I. (2015). BIM-based qualıty control for safety issues in
the desıgn and constructıon phases. Archnet-Ijar, 9(3).
Tang, P., & Akinci, B. (2012). Formalization of workflows for extracting bridge surveying
goals from laser-scanned data. Automation in Construction, 22, 306-319.
Tang, P., Huber, D., & Akinci, B. (2011). Characterization of laser scanners and algorithms
for detecting flatness defects on concrete surfaces. Journal of Computing in Civil
Engineering, 25(1), 31-42.
Wang, Q., Cheng, J. C., & Sohn, H. (2017). Automated estimation of reinforced precast
concrete rebar positions using colored laser scan data. Computer‐Aided Civil and
Infrastructure Engineering, 32(9), 787-802.
Wang, Q., & Kim, M.-K. (2019). Applications of 3D point cloud data in the construction
industry: A fifteen-year review from 2004 to 2018. Advanced Engineering Informatics, 39,
306-319.
Wang, Q., Kim, M.-K., Cheng, J. C., & Sohn, H. (2016). Automated quality assessment of
precast concrete elements with geometry irregularities using terrestrial laser
scanning. Automation in Construction, 68, 170-182.
Zhang, S., Teizer, J., Lee, J.-K., Eastman, C. M., & Venugopal, M. (2013). Building
information modeling (BIM) and safety: Automatic safety checking of construction models
and schedules. Automation in Construction, 29, 183-195.
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As-built spatial data are useful in many construction-related applications, such as quality control and progress monitoring. These data can be collected using a number of imaging and time-of-flight-based (e. g., laser scanning) sensor methods. Each application will demand a particular level of data accuracy and quality, yet little information is available to help engineers choose the most cost-effective approach. This paper presents an analytical and quantitative comparison of photogrammetric, videogrammetric, and time-of-flight-based methods. This comparison is done with respect to accuracy, quality, time efficiency, and cost. To this end, representative image-based three-dimensional reconstruction software and commercially available hardware (two cameras and a time-of-flight-based laser scanner) are evaluated. Spatial data of typical infrastructure (two bridges and a building) are collected under different settings. The experimental parameters include camera type, resolution, and shooting distance for the imaging sensors. By comparing these data with the ground truth collected by a total station, it is revealed that video/photogrammetry can produce results of moderate accuracy and quality but at a much lower cost as compared to laser scanning. The obtained information is useful to help engineers make cost-effective decisions and help researchers better understand the performance impact of these settings for the sensor technologies. DOI: 10.1061/(ASCE)CO.1943-7862.0000565. (C) 2013 American Society of Civil Engineers.
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Accurate and rapid assessment of the as-built status on any construction site provides the opportunity to understand the current performance of a project easily and quickly. Rapid project assessment further identifies discrepancies between the as-built and as-planned progress, and facilitates decision making on the necessary remedial actions. Currently, manual visual observations and surveying are the most dominant data capturing techniques but they are time-consuming, error-prone, and infrequent, making quick and reliable decision-making difficult. Therefore, research on new approaches that allow automatic recognition of as-built performance and visualization of construction progress is essential. This paper presents and compares two methods for obtaining point cloud models for detection and visualization of as-built status for construction projects: (1) A new method of automated image-based reconstruction and modeling of the as-built project status using unordered daily construction photo collections through analysis of Structure from Motion (SfM); (2) 3D laser scanning and analysis of the as-built dense point cloud models. These approaches provide robust means for recognition of progress, productivity, and quality on a construction site. In this paper, an overview of the newly developed automated image-based reconstruction approach and exclusive features which distinct it from other image-based or conventional photogrammetric techniques is presented. Subsequently the terrestrial laser scanning approach carried out for reconstruction and comparison of as-built scenes is presented. Finally the accuracy and usability of both of these techniques for metric reconstruction, automated production of point cloud models, 3D CAD shape modeling, and as-built visualizations is evaluated and compared on eight different case studies. It is shown that for precise defect detection or alignment tasks, image-based point cloud models may not be as accurate and dense as laser scanners' point cloud models. Nonetheless image-based point cloud models provide an opportunity to extract as-built semantic information (i.e., progress, productivity, quality and safety) through the content of the images, are easy to use, and do not need add burden on the project management teams by requiring expertise for data collection or analysis. Finally image-based reconstruction automatically provides photo alignment with point cloud models and enables image-based renderings which can remarkably impact automated performance monitoring and as-built visualizations.