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Article

A Case Study of 3D Scanning Techniques in Civil Engineering Using the Terrestrial Laser Scanning Technique

by
Artur Piekarczuk
1,*,
Aleksandra Mazurek
1,
Jacek Szer
2 and
Iwona Szer
2
1
Building Structures, Geotechnics and Concrete Department, Instytut Techniki Budowlanej, 00-611 Warsaw, Poland
2
Faculty of Civil Engineering, Architecture and Environmental Engineering, Lodz University of Technology, 90-924 Lodz, Poland
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(12), 3703; https://doi.org/10.3390/buildings14123703
Submission received: 23 October 2024 / Revised: 12 November 2024 / Accepted: 19 November 2024 / Published: 21 November 2024
(This article belongs to the Section Construction Management, and Computers & Digitization)
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Abstract

:
This paper reviews the measurement challenges associated with 3D scanning techniques in civil engineering, exploring the practical aspects of scanning buildings and complex surfaces through various case studies. The paper details the conventional use of Terrestrial Laser Scanning (TLS) for reconstructing the technical documentation of a hall. Then it describes an unconventional application of this technique for measuring an External Thermal Insulation Composite System (ETICS) wall, aimed at detecting microdeformations caused by environmental factors controlled within a climatic chamber. Subsequently, the measurements of the insulated wall were repeated using a metrological grade laser scanner. The numerical data were analysed with inspection engineering methods. The deformation maps and displacements of selected reference points were compared. This approach yielded qualitative and quantitative results. The qualitative results, i.e., the distribution of deformations in the form of a map, turned out to be consistent. However, quantitative results show a significant discrepancy in extreme cases of up to 70%.

1. Introduction

The development of a design and the subsequent prototype of a product or a full-scale facility is an essential part of the implementation of new technologies [1,2]. Design is a complex and labour-intensive process because, in the initial stages of development, various design concepts and optimisation options are considered [3]. Modern design methodologies are extensively supported by numerical techniques at nearly every stage of the development process. Prototyping most often involves optimising selected product parameters [4]. In the simplest cases, these are unidirectional design-and-execute activities. In more advanced models, multithreaded optimisation couplings are implemented, such as digital shadow and digital twin models [5,6,7]. In such models, the accuracy of the reproduction of the test object plays an important role. Essential to the prototyping process is the integration of different imaging techniques to achieve the appropriate scan accuracy [8].
Three-dimensional (3D) scanning technology is advancing rapidly across almost every field of technology and science. These include the identification and main applications of 3D scanning from an industrial perspective [9,10] and their applications outside civil engineering [11,12,13,14,15]. Very often, 3D scanning systems are used to scan large cubic objects and engineering and technical infrastructure. Examples of such an application are the quality control process in a residential building [16], the larger-scale building of 3D models used for control during tectonic activity [17], as well as the deformation of structures such as bridges and tunnels, and ground deformation in landslides and unstable rock masses [18]. It is extremely important to use 3D scanning methods in support of supporting laboratory research. This approach was presented by Feng et al. [19]. An innovative method for the detection of geometric imperfections was presented in objects made of carbon fibre reinforced polymer (CFRP). This made it possible to predict the geometry of the models and develop a method to reduce deflection. An even more original approach using 3D scanning methods is proposed in the publication [20]. Tzortzinis, et al. used a scanning technique to inspect corroded bridge components and then developed an original way of numerically modelling such components. These techniques are primarily employed for the noninvasive observation of material structure and volumetric defects. Due to the complexity of the technology, they are not used to map the surface of an object. For rapid surface mapping, simpler devices are employed, namely optical scanners that predominantly use structured light or laser beams. Scanning involves the optical acquisition of geometric data for physical objects [21], in which artificially generated external light sources are used. The acquired information is digitally stored in the form of a point cloud, which represents data on the spatial location of the test object. Following the generation of the point cloud, post-processing steps are undertaken, which involve cleaning artefacts (random errors) and the meshing process, that is, combining with a mesh in standard triangle language (STL) [22]. Currently, the most widely used scanning techniques use different types of projection units [23]. There are active units, i.e., laser scanners, structured light scanners, and passive scanners, which use natural light reflected from the object or radiation in the infrared band. Several surface image acquisition techniques are currently used in measurement practice [24]: photogrammetry [25], time-of-flight (TOF) [26,27] interferometry, laser triangulation (LT) [28], and structured light scanner (SLS) [29]. A scanner is usually built with two basic modules. The first is a projector that generates a beam of light, and the second is a head equipped with a set of sensitive cameras that track changes in the intensity of light reflected from the object being scanned. For scanning large objects such as buildings and engineering structures, passive scanners, such as the Terrestrial Laser Scanner (TLS), are used due to their efficiency and relatively good imaging accuracy. Scans are performed in several or dozens of measurement zones so that neighbouring scanning zones share common imaging areas within their range. The scanning time from a single observation point depends on the set imaging accuracy and ranges from several to tens of minutes. Devices using laser lines are used to image small parts and details. The type of laser light is important in terms of measurement accuracy and the quality of the images obtained [30]. In this category, there are two types: red laser and blue laser [31]. Red laser light is used in the early development of scanning. This type of laser operates close to the infrared spectrum at a wavelength of approximately 670 nm. The red laser is commonly used in industrial automation, typically on inspection lines during production. The disadvantage of the red laser is its low accuracy when mapping shiny surfaces; however, its advantages include speed and intensity of illumination, which facilitate the observation of fast-moving objects. Blue lasers are used in modern measuring equipment for laboratory and production applications. This type of laser operates at approximately 405 nm within the visible-light spectrum, close to the ultraviolet spectrum. The blue laser is much better suited to measure transparent and translucent materials, reflective and mirror surfaces, as well as organic surfaces such as wood [32]. This technique enables the imaging of three-dimensional objects with intricate structures and small dimensions. Reference points in the form of markers affixed to the surface are necessary to perform a pop-up scan. Another advantage is the possibility of using manually controlled scanners, which significantly enhances the mobility of such devices. Structured light scanners utilise a digital projector. The exposure process is more demanding than that of a laser scanner. This type of equipment is used to register individual images, which are then assembled together. When scanning shiny surfaces (for example, steel), it is necessary to use special antireflection sprays [33]. This preparation allows for the application of a thin layer with a thickness on the order of tens of micrometres, thereby protecting the reflective surface. The best results are achieved by using specially stabilised tripods. Structured light scanning has the advantage of providing texture and colour mapping of the test object. In contrast, 3D scanning is used solely to acquire a digital image of the test object, which has no useful function beyond graphical visualisation. Beyond the precision of image acquisition, which depends on the exposure method and the accuracy of the device, disturbance of the measurement environment, such as the intensity of natural light during measurements, may also be important. This type of environmental disturbance is minimised under laboratory conditions, but still occurs in in situ measurements. After the point cloud is obtained, another process associated with post-processing follows. In further analysis, point cloud or STL mesh can be used in various ways. The main task is to obtain CAD-type geometry. Reverse engineering is used for this purpose [34], coupled with scanning, inspection [35,36], and control techniques [37]. The concept of reverse engineering is to recreate a model based on an existing pattern without access to detailed technical documentation. In the case of scanning, this pattern is represented by a point cloud. Reconstruction is typically performed using dedicated graphics software and aims to achieve a match as close as possible to the original pattern [38]. The quality of this matching is assessed using inspection software, which measures the deviation of the reconstructed model in relation to the reference model. The final output is a fully scalable model optimised for numerical calculations.
This paper takes a review-based approach and includes a case study focused on the analysis of a civil engineering structure. It presents examples of the application of two distinct scanning techniques within their respective dedicated ranges and explores the potential for the unconventional use of the Terrestrial Laser Scanning (TLS) scanner in conducting high-precision measurements of large surfaces. Both 3D scanning techniques are evaluated with regard to their qualitative and quantitative measurement accuracy.
The novelty presented in this article lies in the comparison of two fundamentally different scanning techniques in terms of their capabilities and suitability for precise deformation measurements. The primary objective of the study is to evaluate these methods based on selected examples and to investigate the validity of their potential interchangeability. The analysis aims to provide information on whether different scanning techniques can be used interchangeably, providing a deeper understanding of their advantages and limitations in the context of civil engineering applications.

2. Test Objects and Scanning Methods

2.1. Industrial Hall

The test facility is an industrial hall from the previous century that is currently out of use. The main part of the facility is a six-aisle, partially basement hall. The roof of the hall is painted and has various spans ranging from 5.4 to approximately 6.4 m. The spacing of the steel columns supporting the roof structure is approximately 6.5 m. The roof is in a state of significant deterioration, with numerous cavities. It is supported by solid brick masonry walls and two branched columns made of steel channels. The columns are constructed from C140 channels spaced approximately 100 mm apart, connected by battens and gusset plates. Photographs of the current state of the facility are shown in Figure 1. Figure 1a and Figure 2b show a photograph of the roof of the building taken from a drone. The photographs illustrate the extent of the damage. Figure 2b,c show the interior of the building, including the technical condition of the superstructure and the damage to the roof as seen from the inside. The facility does not have technical documentation. The purpose of the analyses is to reconstruct the object’s basic technical documentation by reverse engineering and digitising the object’s damage. The 3D imaging was performed with a Faro Focus volume scanner [39,40]. The scanner has a range of 0.6 to 70 m and a resolution of up to 165 megapixels. The scanner enables image acquisition in the form of a point cloud. In addition, an integrated colour camera allows for the colour overlay of scanned surfaces [41]. The scanning speed is 2 million points per second. The scanner’s field of view is 360° horizontally and 300° vertically. The basic parameters of the Faro Focus 3D [42] and Terrestrial Laser Scanning (TLC) scanner are summarised in Table 1.
The reading accuracy and autocalibration error analysis compared to similar devices of this type are presented in [43]. Finally, it was found that the random noise from reading the coordinates of the point cloud does not contribute significant errors when scanning at short distances. The usefulness of the Faro Focus system was even confirmed in the article [44]. This paper compares individual systems (the Terrestrial Laser Scanner [TLS], Mobile Mapping System [MMS], and Unmanned Aerial Vehicle [UAV]) and considers the possibility of compiling them. In the realised example, one Faro Focus scanning device was used. The image was exiled from the outside and inside of the object from a total of 55 observation positions. After taking individual scans, individual images were combined into a 3D image using dedicated Faro Scene software (v. 2021.4.0.8610) [45]. Furthermore, ReCap Pro (v. 2023.1) reverse engineering software [46] and AutoCad (v. 2023.1.6) software were used to reproduce the drawing documentation.

2.2. ETICS Wall Sample

The large area object is a wall with insulation from External Thermal Insulation Composite Systems (ETICS) [47,48]. The object, measuring 3.6 m width and 2.8 m height, was tested under laboratory conditions using a climate chamber. The EITCS insulation is mounted on a masonry wall made of aerated concrete blocks embedded in a rigid steel frame. The thermal insulation layer is made of expanded polystyrene (EPS) with a fire-retardant strip made of mineral wool (MW) [49]. The test model, along with the test stand, is shown in Figure 2. Figure 2a shows a test object with a visible insulation layer (before applying the finishing coat). The dark band is the mineral wool, and the remaining surface is covered with polystyrene. Figure 2b illustrates a cross section of the test object with a detailed description of the constituent layers. Figure 2c shows a view of the test chamber, and Figure 2d shows the complete test object before positioning in the test chamber. Finally, the test object is delivered to the chamber and fixed with special clamps. The whole constitutes a sealed system with the possibility of introducing climatic influences (temperature, rain, freezing). The range of thermal and humidity exposures included exposure cycles according to EN 16383 [50]. The set of impacts includes three cycles: heating and wetting (HW), heating and cooling (HC), and wetting, freezing, and thawing (WFT). The purpose of the analysis is to evaluate the extent of the deformation and to measure the displacement of the outer layer of the ETICS wall due to environmental loads after it has undergone all impact cycles. Measurements were conducted in two variants. The first was a measurement taken after the wall was installed, but before the climatic impact (reference scan), and the last measurement was taken after all the cycles of climatic impact had passed (operational scan). Reference and operational imaging was performed with the FreeScan UEPro 3D laser scanner [51] in photogrammetry mode. The basic parameters of the scanner are summarised in Table 2.

3. Results

3.1. Industrial Hall Faro Focus Scanner

Based on the 3D image of the object, a post-press was performed using the reverse engineering method. This task involves the creation of three-dimensional models based on laser scans. For this task, the Faro Scene was first used [52,53]. In this software, individual images were combined, and then the 3D point cloud image was implemented in Autodesk ReCap Pro [54] and Autodesk Revit [55]. Ultimately, a three-dimensional CAD model is obtained [56] and inserted into the point cloud. Figure 3a shows the 3D scan outline, along with the solid in CAD format and the 3D model of the object after point cloud removal (Figure 3b). The drawing contains the external outline of the solid with all details and the internal location of the structural elements. This three-dimensional CAD model serves as a foundation for creating various technical drawings, such as projections, sections, and more.
The 3D scan is fully scalable and can be used for damage inventory, providing detailed information about the size, shape, location of structural fragments, and other relevant characteristics. The CAD drawing (Figure 4) generated from the 3D scan can serve as a basis for recreating the technical documentation [57]. This approach to documentation allows for more meticulous building maintenance and, in certain cases, facilitates the restoration of architectural details.

3.2. Faro Focus TLS Scanner for External Wall Insulated

For large objects, terrestrial laser scanning (TLS) is generally sufficient for reconstructing building documentation. However, practical applications may require redundant scanning. In the context of scanning insulated walls, resolution and quality are crucial. Given that TLS scanning is typically conducted in situ for observing insulated walls on large objects, such as multi-family buildings, the resolution and accuracy of the scan should align with the object’s characteristics as described in Section 3.1. For this specific application, the Faro Focus scanner was configured with a resolution of 1/4 and a quality setting of level four. Figure 5 illustrates the wall scanning process and the resulting point cloud.
Due to laboratory space constraints, the scans were captured from three positions at an approximate distance of 3 m from the test object. Two scanning sessions were conducted: a reference scan before the climate chamber testing (as described in Section 2.2) and a second scan after the climate testing. In total, the combined scans comprised 777,645 nodes. The two scans were superimposed and compared using Geomagic Control X (v.2023.3) inspection software.

3.3. External Insulated Wall FreeScan UEPro Laser Scanner

The first set of measurements was performed with a FreeScan UEPro laser scanner. Scanning required several changes in working position. The post-processing was then performed using Geomagic Control X. control software. The process of acquiring and assembling images of a large surface object is shown in Figure 6. Exposure of the test object (Figure 6a) results in a point cloud (Figure 6b) that represents the scanned surface. After removing artefacts and optimising the points, an STL mesh is created. Identical operations are performed on objects before and after climate impact. Figure 6c shows both models before, and Figure 6d after, assembly. The common reference surface of both models was a steel perimeter frame. The frame was painted with matte grey paint, which did not prevent the reflection of laser light. In addition, reference markers were placed on the surface of the frame for accurate positioning of the frame shape. The single model scan contained 4,994,811 nodes.
Exposure of the test object (Figure 6a) resulted in a point cloud (Figure 6b) representing the scanned surface. Following the removal of artefacts and the optimisation of the point cloud, an STL mesh was generated. These identical procedures were applied to the object both before and after exposure to climatic impacts. Figure 6c illustrates both models before assembly, while Figure 6d shows them after assembly. A steel perimeter frame served as the common reference surface for both models. When the models were tested, an inspection analysis was performed. The results of the analysis are presented in Section 3.4.

3.4. Comparison of FreeScan UEPro and Faro Focus Scans

Figure 7 presents the inspection results for two distinct laser scanners: FreeScan UEPro and Faro Focus. The results are displayed both qualitatively in a colour deformation map and quantitatively in the form of deformation values at specific points on the wall surface. These points represent the locations of maximum and minimum deflections within the region of greatest deformation.
Table 3 summarises the statistics of the measurement parameters in relation to the deformation map.
Figure 7a shows the measurement results with the FreeScan UEPro. In Figure 7b, with the FaroFocus scanner, the deformation map obtained with both sets of measurements looks similar.
The deformation maps are the histograms of the measurements, which illustrate the frequency of a random variable within specified intervals. In this instance, the histograms depict the deformation of the operational model relative to the reference model, measured in a direction normal to the reference surface or in the fitting plane. The red lines indicate the tolerance interval of ±0.1 mm, which was initially adopted as an analysis criterion. Table 1 and Table 2 summarise the measurement statistics. Table 1 presents the statistics for the entire deformation map, while Table 2 focusses on the selected reference points A, B, C, and D. The number of nodes refers to the number of nodes extracted from the point cloud and used for statistical calculations. For the deformation map, the point cloud encompasses the entire model area. In contrast, for the reference points, only the nodes located within a 3 mm diameter sphere surrounding the respective reference point are considered. Both scanners detected local geometry perturbations at the same location. A horizontal strip of convexity is visible in the upper part of the model (red zone), and a local indentation is visible in the lower left corner (blue zone). The protrusion occurred when a different insulating material was used, while the indentation was caused by the excessive pressure of the sample against the climate chamber. Interestingly, both scanners detected the outlines of the insulation boards that are hidden under the foundation coat. The extreme deformation values shown on the scales of the two sets differ from each other by up to 45%. In addition, four measurement points on the surface of the model were selected for qualitative testing. Two describe the largest convexity, and two describe the largest concavity. Comparison of these results indicates agreement on the direction of deformation (concavity, indentation). Discrepancies in the four test points of the Faro Focus model with respect to the FreeScan UEPro model range from 12% to 73%. This is only a sample of test data and is not an evaluation of the performance of the scans tested. The analysis methodology, the test procedure, and the detailed measurement results are presented in [58,59].

4. Discussion

The most important problem in scanning large objects was recognised in the publication of Almukhtar et al. [60] and relates to the quality of the data obtained and the way the data are processed. From the authors’ experience gained during this task, it is clear that maintaining uniform scanning conditions plays an important role in acquiring useful images, especially for in situ work and long-term measurements. Changing environmental conditions, such as temperature and lighting during scanning [61] at different intervals, can cause excessive errors [34]. Another troublesome problem is the quality of numerical data acquired during scanning and processed in post-processing. It turns out that scanning surfaces of different textures, dimensions, and transparency (for example, a brick wall and window glass) generates divergent quality point clouds. Thus, it is difficult to apply a uniform noise removal algorithm. In this case, it seems simplest to remove the troublesome quality areas of the point cloud just to preserve the relatively good data quality of the main part of the model. However, the accuracy of the point cloud of the Faro Focus scanner is sufficient to reproduce the drawing documentation of the object with an accuracy of approximately ±3 mm, as presented in Section 3.1.
Another problem concerns the possibility of using TLS (Faro Focus) techniques to make precise measurements of small deformations in large areas of an object. An example of such a task is the analysis of the influence of the environment on the deformation of the outer insulation layer of multistory buildings that are undergoing diagnosis or renovation. Precise measurements of this type are problematic for two reasons. The first is the technical accessibility of the surface to be surveyed. Buildings with ETICS external insulation are customarily multistory structures, so access to the surface of the object is limited by the need for precision laser scanners. This type of scanner requires exposure at a short working distance of up to 0.3 m. Maintaining this condition requires the construction of expensive and time-consuming scaffolding. The easiest way is to use a TLC scanner and observe the object from the ground surface. The second reason is the measurement accuracy associated with the device used. To date, the testing of large ETICS wall surfaces has been conducted under laboratory conditions [59]. An abbreviated description of these studies is presented in Section 2.2. The concept of comparing the quality of scans obtained with different devices is presented in Section 3.4. The results of scanning with Faro Focus and FreeScan UEPro are compared there. These scanners are completely different from each other in terms of image acquisition methods and measurement accuracy. Two assessment methods were adopted: qualitative and quantitative. The qualitative method involved evaluating the deformation of the entire observation area and was usually presented in the form of colour deformation maps. The quantitative method consisted of assessing the deformation at selected points, analogous to point sensors. The results of the qualitative method showed agreement in terms of the deformation map of the distribution of the examined model (Figure 7). The colour-coded deformation images in both scanners were very similar. A horizontal protrusion strip (red) was visible at the mineral wool installation site (description in Section 2.2). The technical defect created when the sample was installed was clearly visible (the dark blue L-shaped band in the lower left corner). The surface concavity in the centre of the sample was also recognisable (blue), and even the place where the polystyrene boards were joined (horizontal and vertical lines in dark blue) could be seen. Note that the scan made with a laser (FreeScan UEPro) was much more accurate. The measurement histograms below the deformation maps (Figure 7) were configured differently. This indicated that FreeScan UEPro represented a non-uniform distribution, meaning that the point cloud was precisely aligned with the deformed surface. The Faro Focus histogram was almost symmetric with respect to the mean, indicating that the point cloud spread smoothly regardless of the surface. The predetermined tolerance range of ±0.1 mm (the marked red area on the histogram) encompassed a broader range with the FreeScan UE Pro scanner. Table 3 presents the metrics for both qualitative measurements, showing significant differences in the number of nodes. The FreeScan UEPro scanner generated almost 5 million nodes, while Faro Focus generated more than 6 times fewer. The RMS and variance parameters were also more precise for FreeScan UEPro. The quantitative comparison for the reference points was divergent (Table 4). The number of nodes adopted for the calculation of the statistics at the reference points of the two scanners differed significantly. The FreeScan UEPro scanner had between 10 and 21 nodes available for calculation in the 3 mm sphere, while the Faro Focus scanner had a maximum of 2. This was due to the density of point clouds per unit area. The consequence of changing the point cloud density was, of course, a loss of measurement accuracy. If the FreeScan UEPro scan was considered as a reference, the loss of the measurement accuracy of the Faro Focus ranged from 12% to 73%. In principle, this was not a disadvantage of the Faro Focus because the device differed significantly in accuracy parameters compared to the FreeScan UEPro. Due to such a large discrepancy in results, quantitative evaluation seemed problematic. In this case, it seemed reasonable to combine two different devices in one test. Such an approach was considered in the publication [44].

5. Conclusions

The primary objective of this paper is to compare the image acquisition capabilities of different devices, which has practical significance for the acquisition of measurement techniques. Specifically, it enables both quantitative and qualitative comparisons of the analysis results when observing a common test object, namely a large-scale model of an External Thermal Insulation Composite System (ETICS) insulation wall.
At the heart of this research is the examination of the similarities and differences between the scanning techniques used. The findings confirm the hypothesis that different 3D scanning technologies produce consistent qualitative, but quantitatively divergent, results. It is important to note that the results presented in this article were obtained under controlled laboratory conditions and at short distances. The authors acknowledge the limitations associated with these conditions, and, as such, the findings should be considered preliminary. This study marks the beginning of a broader research effort, with ongoing work focused on conducting similar measurements under real-world (in situ) conditions.

Author Contributions

Conceptualization, A.P., J.S. and A.M.; methodology, A.P. and A.M.; software, A.P.; validation, A.P.; formal analysis, A.P.; investigation, A.P., A.M. and I.S.; resources, A.P., A.M., J.S. and I.S.; data curation, A.P. and A.M. writing—original draft preparation, A.P. and J.S.; writing—review and editing, A.P.; visualization, A.P. and A.M.; supervision, A.P.; project administration, A.P. and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Higher Education as part of the projects NZK-119/2024 and NZK-117/2024.

Data Availability Statement

The data presented in this study are available from the corresponding author on request (due to privacy).

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the study’s design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Relich, M. Product Development: State of the Art and Challenges. In Decision Support for Product Development: Using Computational Intelligence for Information Acquisition in Enterprise Databases; Springer: Cham, Switzerland, 2021; pp. 1–26. [Google Scholar]
  2. Amigo, C.R.; Iritani, D.R.; Rozenfeld, H.; Ometto, A. Product Development Process Modeling: State of the Art and Classification. In Smart Product Engineering, Proceedings of the 23rd CIRP Design Conference, Bochum, Germany, 11–13 March 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 169–179. [Google Scholar] [CrossRef]
  3. Wynn, D.C.; Clarkson, P.J. Process Models in Design and Development. Res. Eng. Des. 2018, 29, 161–202. [Google Scholar] [CrossRef]
  4. Stănăşel, I.; Buidoş, T.; Crăciun, D. Rapid Prototyping Technology and 3d Scanning Verification. Case study. Rev. De Tehnol. Neconv. 2017, 21, 48. [Google Scholar]
  5. Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital Twin in Manufacturing: A Categorical Literature Review and Classification. IFAC-Pap. 2018, 51, 1016–1022. [Google Scholar] [CrossRef]
  6. Botín-Sanabria, D.M.; Mihaita, S.; Peimbert-García, R.E.; Ramírez-Moreno, M.A.; Ramírez-Mendoza, R.A.; Lozoya-Santos, J.J. Digital Twin Technology Challenges and Applications: A Comprehensive Review. Remote Sens. 2022, 14, 1335. [Google Scholar] [CrossRef]
  7. Yu, W.; Patros, P.; Young, B.; Klinac, E.; Walmsley, T.G. Energy Digital Twin Technology for Industrial Energy Management: Classification, Challenges and Future. Renew. Sustain. Energy Rev. 2022, 161, 112407. [Google Scholar] [CrossRef]
  8. Lipowiecki, I.; Rządkowski, W.; Zapał, W.; Kowalik, M. Combining the Technology of Long-Range Laser 3D Scanners and Structured Light Handheld 3D Scanners to Digitize Large-Sized Objects. Adv. Sci. Technol. Res. J. 2023, 17, 196–205. [Google Scholar] [CrossRef]
  9. Javaid, M.; Haleem, A.; Pratap Singh, R.; Suman, R. Industrial Perspectives of 3D Scanning: Features, Roles and It’s Analytical Applications. Sens. Int. 2021, 2, 100114. [Google Scholar] [CrossRef]
  10. Daneshmand, M.; Helmi, A.; Avots, E.; Noroozi, F.; Alisinanoglu, F.; Arslan, H.S. 3D Scanning: A Comprehensive Survey. arXiv 2018, arXiv:1801.08863. [Google Scholar]
  11. Mihić, M.; Sigmund, Z.; Završki, I.; Butković, L.L. An Analysis of Potential Uses, Limitations and Barriers to Implementation of 3D Scan Data for Construction Management-Related Use—Are the Industry and the Technical Solutions Mature Enough for Adoption? Buildings 2023, 13, 1184. [Google Scholar] [CrossRef]
  12. Marshall, G.F.; Stutz, G.E. Handbook of Optical and Laser Scanning; CRC Press: Boca Raton, FL, USA, 2018; ISBN 9781315218243. [Google Scholar]
  13. De Luca, D.; Del Giudice, M.; Grasso, N.; Matrone, F.; Osello, A.; Piras, M. Handheld Volumetric Scanner for 3D Printed Integrations of Historical Elements: Comprasion and Resilts. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2019, 42, 381–388. [Google Scholar] [CrossRef]
  14. Nagy, Z.; Kelemen, A.; Sánduly, A. 3D Scanning Applications in Structural Design. In Proceedings of the IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures—Report, Prague, Czech Republic, 25–27 May 2022; pp. 1079–1086. [Google Scholar]
  15. van Brügge, L.; Çetin, K.M.; Koeberle, S.J.; Thiele, M.; Sturm, F.; Hornung, M. Application of 3D-Scanning for Structural and Geometric Assessment of Aerospace Structures. CEAS Aeronaut. J. 2023, 14, 455–467. [Google Scholar] [CrossRef]
  16. Peansupap, V.; May, A.M. Development of a System for Measuring Surface Slope with Point Cloud Data. In Lecture Notes in Civil Engineering; Springer Science and Business Media Deutschland GmbH: Berlin, Germany, 2024; Volume 369, pp. 507–516. ISBN 9789819940486. [Google Scholar]
  17. Tran, H.H.; Vu, H.Q.; Van Tran, A. Application of FARO Focus 3D S350 Terrestrial Laser Scanner in Building 3D Models of Potential Areas of Landslides and Rocks—Case Study in Ha Giang Province, Vietnam. In Proceedings of the International Conference on Sustainability in Civil Engineering, Hanoi, Vietnam, 25–27 November 2022; Springer: Singapore, 2022; Volume 344, pp. 703–710. [Google Scholar]
  18. Shen, N.; Wang, B.; Ma, H.; Zhao, X.; Zhou, Y.; Zhang, Z.; Xu, J. A Review of Terrestrial Laser Scanning (TLS)-Based Technologies for Deformation Monitoring in Engineering. Meas. J. Int. Meas. Confed. 2023, 223, 113684. [Google Scholar] [CrossRef]
  19. Feng, P.; Zou, Y.; Hu, L.; Liu, T.Q. Use of 3D Laser Scanning on Evaluating Reduction of Initial Geometric Imperfection of Steel Column with Pre-Stressed CFRP. Eng. Struct. 2019, 198, 109527. [Google Scholar] [CrossRef]
  20. Tzortzinis, G.; Ai, C.; Breña, S.F.; Gerasimidis, S. Using 3D Laser Scanning for Estimating the Capacity of Corroded Steel Bridge Girders: Experiments, Computations and Analytical Solutions. Eng. Struct. 2022, 265, 114407. [Google Scholar] [CrossRef]
  21. Flugge, J.; Wendt, K.; Danzebrink, H.; Abou-zeid, A. Optical Methods for Dimensional Metrology in Production Engineering. CIRP Ann. Manuf. Technol. 2002, 51, 685–699. [Google Scholar]
  22. Szilvási-Nagy, M.; Mátyási, G. Analysis of STL Files. Math. Comput. Model. 2003, 38, 945–960. [Google Scholar] [CrossRef]
  23. Haleem, A.; Javaid, M.; Singh, R.P.; Rab, S.; Suman, R.; Kumar, L.; Khan, I.H. Exploring the Potential of 3D Scanning in Industry 4.0: An Overview. Int. J. Cogn. Comput. Eng. 2022, 3, 161–171. [Google Scholar] [CrossRef]
  24. Bell, T.; Li, B.; Zhang, S. Structured Light Techniques and Applications. In Wiley Encyclopedia of Electrical and Electronics Engineering; Purdue University: West Lafayette, IN, USA, 2016; pp. 1–24. [Google Scholar] [CrossRef]
  25. Schenk, T. Introduction to Photogrammetry; Ohio State University: Columbus, OH, USA, 2005; ISBN 9781609181765. [Google Scholar]
  26. Lopez Paredes, A.; Song, Q.; Conde, M.H. Performance Evaluation of State-of-the-Art High-Resolution Time-of-Flight Cameras. IEEE Sens. J. 2023, 23, 13711–13727. [Google Scholar] [CrossRef]
  27. Baqersad, J.; Poozesh, P.; Niezrecki, C.; Avitabile, P. Photogrammetry and Optical Methods in Structural Dynamics—A Review. Mech. Syst. Signal Process. 2017, 86, 17–34. [Google Scholar] [CrossRef]
  28. Ding, D.; Ding, W.; Huang, R.; Fu, Y.; Xu, F. Research Progress of Laser Triangulation On-Machine Measurement Technology for Complex Surface: A Review. Meas. J. Int. Meas. Confed. 2023, 216, 113001. [Google Scholar] [CrossRef]
  29. Georgopoulos, A.; Ioannidis, C.; Valanis, A. Assessing the Performance of a Structured Light Scanner. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2010, 38, 251–255. [Google Scholar]
  30. Ao, S.; Gelman, L. Advances in Electrical Engineering and Computational Science; Springer: Berlin, Germany, 2009; Volume 39, ISBN 9789048123100. [Google Scholar]
  31. Dawda, A.; Nguyen, M. Comparison of Red versus Blue Laser Light for Accurate 3D Measurement of Highly Specular Surfaces in Ambient Lighting Conditions. In Proceedings of the Communications in Computer and Information Science, Auckland, New Zealand, 28–29 January 2021; Springer Science and Business Media Deutschland GmbH: Berlin, Germany, 2021; Volume 1386, pp. 300–312. [Google Scholar]
  32. Wedgbrow, G. Plant & Works Engineering. Tonbridge, UK, 2022; pp. 32–33. Available online: https://pwemag.co.uk/ (accessed on 22 October 2024).
  33. Franke, J.; Koutecký, T.; Koutný, D. Comparison of Sublimation 3D Scanning Sprays in Terms of Their Effect on the Resulting 3D Scan, Thickness, and Sublimation Time. Materials 2023, 16, 6165. [Google Scholar] [CrossRef] [PubMed]
  34. Helle, R.H.; Lemu, H.G. A Case Study on Use of 3D Scanning for Reverse Engineering and Quality Control. Mater. Today Proc. 2021, 45, 5255–5262. [Google Scholar] [CrossRef]
  35. Yilmaz, B.; Marques, V.R.; Donmez, M.B.; Cuellar, A.R.; Lu, W.E.; Abou-Ayash, S.; Çakmak, G. Influence of 3D Analysis Software on Measured Deviations of CAD-CAM Resin Crowns from Virtual Design File: An in-Vitro Study. J. Dent. 2022, 118, 103933. [Google Scholar] [CrossRef]
  36. Taraben, J.; Morgenthal, G. Automated Linking of 3D Inspection Data for Damage Analysis. In Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020, Sapporo, Japan, 28 June–2 July 2020; CRC Press: London, UK, 2021; pp. 3714–3720. [Google Scholar]
  37. Stałowska, P.; Suchocki, C.; Rutkowska, M. Crack Detection in Building Walls Based on Geometric and Radiometric Point Cloud Information. Autom. Constr. 2022, 134, 104065. [Google Scholar] [CrossRef]
  38. Li, N.; Wang, Y.; Geng, W.; Li, Z. Enhancing Extraction of Two-Dimensional Engineering Drawings from Three-Dimensional Data of Existing Buildings. J. Build. Eng. 2023, 76, 107235. [Google Scholar] [CrossRef]
  39. Stanley, T. Assessment of the FARO 3D Focus Laser Scanner for Forest Inventory. Ph.D. Dissertation, University of Southern Queensland, Darling Heights, Australia, 2013. [Google Scholar]
  40. Kersten, T.P.; Lindstaedt, M. Geometric Accuracy Investigations of Terrestrial Laser Scanner Systems in the Laboratory and in the Field. Appl. Geomat. 2022, 14, 421–434. [Google Scholar] [CrossRef]
  41. Pervolarakis, Z.; Zidianakis, E.; Katzourakis, A.; Evdaimon, T.; Partarakis, N.; Zabulis, X.; Stephanidis, C. Three-Dimensional Digitization of Archaeological Sites—The Use Case of the Palace of Knossos. Heritage 2023, 6, 904–927. [Google Scholar] [CrossRef]
  42. FARO Technologies Inc. Faro Focus Laser Scanner User Manual; Worldwide: Purple Downs, Australia, 2011. [Google Scholar]
  43. Chow, J.C.K.; Lichti, D.D.; Teskey, W.F.; Key, C. Accuracy Assessment of the FARO Focus 3D and Leica HDS6100 Panoramic- Type Terrestrial Laser Scanners through Point-Based and Plane-Based User Self-Calibration. In Proceedings of the FIG Working Week: Knowing to Manage the Territory, Protect the Environment, Evaluate the Cultural Heritage, Rome, Italy, 6–10 May 2012; Volume 610, pp. 6–10. [Google Scholar]
  44. Chiabrando, F.; Sammartano, G.; Spanò, A.; Spreafico, A. Hybrid 3D Models: When Geomatics Innovations Meet Extensive Built Heritage Complexes. ISPRS Int. J. Geo-Inf. 2019, 8, 124. [Google Scholar] [CrossRef]
  45. Rocha, G.; Mateus, L.; Fernández, J.; Ferreira, V. A Scan-to-Bim Methodology Applied to Heritage Buildings. Heritage 2020, 3, 47–65. [Google Scholar] [CrossRef]
  46. Parras, D.; Cavas-Martínez, F.; Nieto, J.; Cañavate, F.J.F.; Fernández-Pacheco, D.G. Reconstruction by Low Cost Software Based on Photogrammetry as a Reverse Engineering Process. In Proceedings of the Lecture Notes in Computer Science, Cairo, Egypt, 2–7 September 2023; Springer: Berlin/Heidelberg, Germany, 2018; Volume 10909, pp. 145–154. [Google Scholar]
  47. Michalak, J. External Thermal Insulation Composite Systems (ETICS) from Industry and Academia Perspective. Sustainability 2021, 13, 13705. [Google Scholar] [CrossRef]
  48. Fernandes, C.; De Brito, J.; Cruz, C.O. Architectural Integration of ETICS in Building Rehabilitation. J. Build. Eng. 2016, 5, 178–184. [Google Scholar] [CrossRef]
  49. Dong, Y.; Kong, J.; Mousavi, S.; Rismanchi, B.; Yap, P.S. Wall Insulation Materials in Different Climate Zones: A Review on Challenges and Opportunities of Available Alternatives. Thermo 2023, 3, 38–65. [Google Scholar] [CrossRef]
  50. EN 16383:2016; Thermal Insulation Products for Building Applications. Determination of the Hygrothermal Behaviour of External Thermal Insulation Composite Systems with Renders (ETICS). European Committee for Standardization: Brussel, Belgium, 2016.
  51. SHINING 3D Tech Co., Ltd. FreeScan UE Pro User Manual; Shining 3D Offices: Hangzhou, China, 2023. [Google Scholar]
  52. Le, Q.; Liscio, E. A Comparative Study between FARO Scene and FARO Zone 3D for Area of Origin Analysis. Forensic Sci. Int. 2019, 301, 166–173. [Google Scholar] [CrossRef] [PubMed]
  53. Dhruwa, L.; Garg, P.K. Generation of 3D Large-Scale Maps Using LiDAR Point Cloud Data. Int. Soc. Photogramm. Remote Sens. 2023, 48, 1–5. [Google Scholar]
  54. Dhruwa, L.; Garg, P.K. Positional Accuracy Assessment of Features Using Lidar Point Cloud. Int. Soc. Photogramm. Remote Sens. 2023, 48, 77–80. [Google Scholar] [CrossRef]
  55. Marzouk, M.; El-Bendary, N. Facility Management of Gas Turbine Power Plants Using 3D Laser Scanning. HBRC J. 2022, 18, 73–83. [Google Scholar] [CrossRef]
  56. Regassa Hunde, B.; Debebe Woldeyohannes, A. Future Prospects of Computer-Aided Design (CAD)—A Review from the Perspective of Artificial Intelligence (AI), Extended Reality, and 3D Printing. Results Eng. 2022, 14, 100478. [Google Scholar] [CrossRef]
  57. Szer, J.; Jeruzal, J.; Szer, I.; Filipowicz, P. Periodic Inspections of Buildings Recommendations, Requirements and Problems; Monography; Lodz University of Technology: Lodz, Poland, 2020; ISBN 978-83-66287-43-3. [Google Scholar]
  58. Sudoł, E.; Piekarczuk, A.; Kozikowska, E.; Mazurek, A. Resistance of External Thermal Insulation Systems with Fire Barriers to Long-Lasting Weathering. Materials 2024, 17, 3113. [Google Scholar] [CrossRef]
  59. Piekarczuk, A.; Sudoł, E.; Mazurek, A. Measurement Analysis of Large-Area Elements of External Thermal Insulation Composite Systems Using 3D Scanning Techniques. Meas. J. Int. Meas. Confed. 2024, 233, 114755. [Google Scholar] [CrossRef]
  60. Almukhtar, A.; Saeed, Z.O.; Abanda, H.; Tah, J.H.M. Reality Capture of Buildings Using 3D Laser Scanners. CivilEng 2021, 2, 214–235. [Google Scholar] [CrossRef]
  61. Blanco, D.; Fernandez, P.; Cuesta, E.; Suarez, C.M.; Beltran, N. Selection of Ambient Light for Laser Digitizing of Quasi-Lambertian Surfaces. Adv. Electr. Eng. Comput. Sci. 2009, 39, 447–457. [Google Scholar] [CrossRef]
Buildings 14 03703 g001
Figure 1. Photograph of the facility: (a,b) exterior view and (c,d) interior view.
Figure 1. Photograph of the facility: (a,b) exterior view and (c,d) interior view.
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Figure 2. ETICS wall test object: (a) insulation layer, (b) description of layers, (c) climate chamber, and (d) object before testing.
Figure 2. ETICS wall test object: (a) insulation layer, (b) description of layers, (c) climate chamber, and (d) object before testing.
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Figure 3. A 3D scan of the facility: (a) interior of the hall and (b) hall from outside.
Figure 3. A 3D scan of the facility: (a) interior of the hall and (b) hall from outside.
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Figure 4. Image of a cubic object: (a) 3D scan and (b) CAD model.
Figure 4. Image of a cubic object: (a) 3D scan and (b) CAD model.
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Figure 5. Isolation wall scanning: (a) scanning process and (b) STL image of the point cloud.
Figure 5. Isolation wall scanning: (a) scanning process and (b) STL image of the point cloud.
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Figure 6. The 3D image acquisition process: (a) scanning, (b) point cloud, (c) image assembly, and (d) result of image assembly.
Figure 6. The 3D image acquisition process: (a) scanning, (b) point cloud, (c) image assembly, and (d) result of image assembly.
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Figure 7. Results of the inspection analysis: (a) measurement results with the FreeScan UEPro scanner and (b) measurement results with the Faro Focus scanner.
Figure 7. Results of the inspection analysis: (a) measurement results with the FreeScan UEPro scanner and (b) measurement results with the Faro Focus scanner.
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Table 1. Basic technical parameters of the Faro Focus scanner.
Table 1. Basic technical parameters of the Faro Focus scanner.
Distance Measurement ErrorAngular PrecisionPosition AccuracyLaser Class
±1 mm19 s10 m: 2 mm/25 m: 3.5 mmClass 1 wavelength 1550 nm
Table 2. Basic technical parameters of the FreeScan UEPro scanner.
Table 2. Basic technical parameters of the FreeScan UEPro scanner.
Scan ModeLight SourceVolumetric AccuracyScan AccuracyScan Speed
Multiple Lines Scan, Single Line Scan26 laser lines, single laser line0.02 + 0.03 mm/m (standard mode)Up to 0.02 mm1,850,000 points/s
Fine Scan7 parallel laser lines0.02 + 0.015 mm/m (built-in photogrammetry mode)
Table 3. Measurement statistics of the deformation map.
Table 3. Measurement statistics of the deformation map.
NameType of Scanner
FreeScan UEProFaroFocus
Number of nodes4,994,811777,645
Min. [mm]−1.2248 −5.6247
Max. [mm]1.22473.6395
Avg. [mm]0.0141 −0.0359
RMS [mm]0.2239 0.3278
Std. Dev. [mm]0.2234 0.3258
Var. [mm]0.0499 0.1062
Table 4. Summary of the measurement statistics for the single reference points labelled A, B, C, and D (Figure 7).
Table 4. Summary of the measurement statistics for the single reference points labelled A, B, C, and D (Figure 7).
NameType of Scanner
FreeScan UEProFaro Focus
Reference PointABCDABCD
Number of nodes191221102121
Min. [mm]0.5898 0.7875 −0.3570 −0.4146 0.5493 0.671 −0.5679 −0.2925
Max. [mm]0.6968 0.8328 −0.2257 −0.3591 0.5693 0.671 −0.4679 −0.2925
avg. [mm]0.6354 0.8100 −0.2997 −0.3839 0.5593 0.671 −0.5179 −0.2925
RMS [mm]0.6363 0.8102 0.3028 0.3844 0.5594 0.671 0.5203 0.2925
Std. Dev. [mm]0.0322 0.0157 0.0429 0.0199 0.01 0 0.05 0
Var. [mm]0.001 0.0002 0.0018 0.0004 0.0001 0 0.0025 0
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MDPI and ACS Style

Piekarczuk, A.; Mazurek, A.; Szer, J.; Szer, I. A Case Study of 3D Scanning Techniques in Civil Engineering Using the Terrestrial Laser Scanning Technique. Buildings 2024, 14, 3703. https://doi.org/10.3390/buildings14123703

AMA Style

Piekarczuk A, Mazurek A, Szer J, Szer I. A Case Study of 3D Scanning Techniques in Civil Engineering Using the Terrestrial Laser Scanning Technique. Buildings. 2024; 14(12):3703. https://doi.org/10.3390/buildings14123703

Chicago/Turabian Style

Piekarczuk, Artur, Aleksandra Mazurek, Jacek Szer, and Iwona Szer. 2024. "A Case Study of 3D Scanning Techniques in Civil Engineering Using the Terrestrial Laser Scanning Technique" Buildings 14, no. 12: 3703. https://doi.org/10.3390/buildings14123703

APA Style

Piekarczuk, A., Mazurek, A., Szer, J., & Szer, I. (2024). A Case Study of 3D Scanning Techniques in Civil Engineering Using the Terrestrial Laser Scanning Technique. Buildings, 14(12), 3703. https://doi.org/10.3390/buildings14123703

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