Analysis on Using 3D Scanning and BIM to Reduce the Physical and Non-Physical Construction Waste for Sustainable Fireproofing of Steel Trusses
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1. Introduction
1.1. Steel Fireproofing
To reduce or even eliminate these deficiencies, the traditional survey process can be replaced by terrestrial laser scanning surveys to generate point clouds that can be processed in Building Information Modeling (BIM) software to create more accurate 3D models, 2D drawings, and quantities.
1.2. Terrestrial Laser Scanning (TLS)
Another notable statistic pertains to the level of detail achievable through 3D laser scanning. This technology can produce highly accurate point clouds, representing millions of individual points in a surveyed space. This wealth of data enables quantity surveyors to create precise and detailed models of existing structures or construction sites, facilitating more accurate quantity takeoffs and cost estimations. The ability to capture intricate details with minimal human intervention enhances the reliability of the surveying process.
1.3. Building Information Modeling (BIM)
Taking all of these aspects into account, the research presented in this paper has a few distinct goals that represent the main research motivation and significance of the paper: (a) analyzing the implications of using 3D laser scanning and BIM software on the calculation of steel truss-fireproofing coverage areas; (b) a sustainable methodology of an efficient and accurate combination that integrates the TLS technology and traditional survey to rigorously calculate the areas to avoid the production of non-physical waste; and (c) a comprehensive analysis of a few key elements that are prone to the shadowing effect that appears in highly complex construction structures that can blunder the 3D model. Furthermore, the proposed methodology for quantity surveying using the 3D TLS and traditional survey techniques for assessing the non-physical waste represents a novelty in advanced research in sustainability in the AEC sector.
Although the research shows no material waste, the innovation within the article presents a more unpleasant type of waste—the so-called non-physical waste which is generated because of cost and time overruns. The cost overruns imply that the actual cost of fireproofing is higher than the approved budget for this work, and that an additional budget needs to go through all administrative procedures from the beginning to be able to complete the work. Thus, it results in a chain effect, resulting in time overruns caused by the fact that, for public investments, approvals for budget supplements involve a time-consuming bureaucratic process, and the construction team that performs the fireproofing works has to stop and relocate both materials and human assets until the necessary budget is approved.
2. Materials and Methods
2.1. The Case Study: Aula Magna Hall of the University of Oradea
The campus’s contemporary character is defined by several key elements:
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The university buildings have undergone an organic evolution, incorporating the architectural nuances of the Secessionist style. This is exemplified by 12 heritage buildings of significant value, constructed between 1911 and 1913, under the guidance of architect Jozsef Vago. The project, while retaining the essence of their spatial organization, originally commenced as a Gendarmerie School [36].
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The implementation of initiatives like “SMART Campus—University of Oradea” demonstrates a dedication to modernization, technological integration, and enhanced accessibility. These efforts underscore a strategic pivot towards innovation and technological advancement within the university’s framework.
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The EU GREEN project, spearheaded by the University of Oradea, places a strong emphasis on sustainability and educational approaches to sustainable development. The university’s active participation in this project signifies a robust commitment to fostering sustainable development principles. Through this project, there is a concerted effort to elevate the level of awareness and engagement among faculty and students concerning sustainable development concepts.
The interdisciplinary nature of these initiatives has fostered a project that serves as a testament to the interpretation of field data, aiming to convert this information into the most precise quantifiable measures for the hall’s rehabilitation project. Additionally, this rehabilitation effort has been augmented by incorporating adjustments to align with the latest fire safety standards.
The metal roof truss over the Aula Magna Hall is a spatial structure composed of transversally arranged truss beams, supported by the reinforced concrete structure, with a span of 20.45 m. The spatial structure is stiffened longitudinally through roof panels fixed over the top chord, at the nodes of the truss beams, and by cross bracings arranged in the plane of the roof. At the lower part, the stiffening of the trusses is achieved through longitudinal beams. Additionally, the bottom horizontal chord of the truss beam is equipped with a fastening system for the suspended ceiling, which uses tension rods, over which mineral wool thermal insulation is laid.
The members of the truss beam are composed of sections made from two equal-flange angles slightly spaced apart, joined together by gusset plates, and fixed through welding. Laminated steel profiles with standardized sections were used. Subsequently, the metal structure was protected against corrosion by at least two layers of paint, which increased the dimensions of the metal profiles identified in the survey. To verify the accuracy of the manual survey and the 3D scanning, it was necessary to establish the initial size of the sections. The initial section of the metal elements was determined based on the standard dimensions of the steel profiles, as given in technical catalogues for hot-rolled steel profiles.
2.2. Traditional Surveying Methods and Terrestrial Laser Scanner Surveying
In the context of our project, the execution of the traditional survey necessitated a specific set of tools, comprising a clipboard, A4 paper sheets, a graphite pencil, a standard tape measure, and a caliper. The methodological approach to the surveying process involved several critical stages.
This methodical process was pivotal in ensuring the collection of precise and comprehensive data necessary for the accurate 3D modeling of the elements in the subsequent phases of the project.
The initial phase involved manually surveying and identifying the profiles through the use of standard catalogues. The traditional surveying process of a steel frame truss structure was made manually, using simple tools like a measuring tape for assessing the dimensions of the steel profiles and a caliper to measure their thickness. Several sketches of sections of the roof structure had to be made on paper on-site in order to mark the specific dimensions of the different measured members. Firstly, the profiles were marked with the actual measured dimensions. Secondly, after completing the manual survey, the steel sections identified were compared with the standard steel sections from technical catalogues for hot-rolled steel profiles.
The steel profile catalogues give the exterior surface of a standard profile based on its section dimensions. The steel structure was protected against rust with at least two coats of paint. In addition, in the corners of the flanges and especially on the lower joints, layers of cemented dust increased the dimensions of the steel sections in comparison with the initial standard section. Thus, in order to manually determine the actual fireproofing surface, the steel sections had to be approximated to a standardized section. Regarding this project, it was not possible to carry out a full survey using traditional techniques because of the lack of safe access to most of the roof elements, but also because the process would not have been time-efficient due to the complexity of the structure. Therefore, the survey process relied mostly on terrestrial laser scanning (TLS) technology.
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Data collection—using the 3D scanner to obtain the necessary point cloud of the specified construction site from different stations,
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Data post-processing—referencing and geo-referencing of the assembly of all the stations from which we made the scanning and applying the necessary computational models for noise reduction and adjustment,
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Geometric modeling of the refined point cloud to generate the 3D model as a mesh or as an object,
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Generating the digital documentation.
For obtaining the 3D point cloud, we used the Trimble X7 laser scanner, which is a professional-grade terrestrial laser scanner designed for high-precision 3D scanning and data acquisition applications in fields such as construction, surveying, and building documentation. Trimble X7’s key features are as follows:
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High-speed scanning: The Trimble X7 is known for its fast and efficient data capture capabilities. It can rapidly collect dense point cloud data, allowing for the quick and comprehensive 3D scanning of structures and environments. The TLS is capable of working at speeds up to 500 kHz (thus capturing up to half a million points per second).
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Integrated imaging: The scanner typically comes equipped with integrated imaging capabilities, such as high-resolution cameras. This allows users to capture colored panoramic images alongside the 3D point cloud data, providing additional visual context.
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Automated operation: The X7 is designed to streamline the scanning process with automation features. Automated workflows and onboard software—we used the Trimble Perspective software 1.1.3 to assist in simplifying data capture, making it more accessible for users with varying levels of expertise.
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User-friendly interface: The device is often designed with a user-friendly interface to enhance the overall user experience. This includes a touch screen or other intuitive controls for easy operation in the field.
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Lightweight and portable: While still being a professional-grade scanner, the Trimble X7 is typically designed to be relatively compact and lightweight compared to some other laser scanning solutions, with the scanner weighing just 5.8 kg and measuring 178 mm (W) × 353 mm (H) × 170 mm (D) (both values without tripod), according to the data provided in Table 1. This enhances its portability and ease of transportation to different job sites.
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Accuracy and range: The scanner is engineered to provide high accuracy in point cloud data. It offers a range suitable for various applications, from close-range detailed scans to capturing data from a distance, with the range accuracy (measured distance between the scanner and object) being 2 mm and the absolute point accuracy of the 3D model being dependent on the scanner–object distance (Table 1, 3D point accuracy section).
The Trimble X7 is commonly used for applications such as building documentation, construction site monitoring, quality control, clash detection, and creating accurate as-built models.
Each individual scan is defined by a station and, thus, is identified by a number, a specified color, and a marked position; hence, the point cloud registration can be performed automatically in the field. This can be performed also with the help of the built-in Inertial Measurement Unit (IMU), which has the ability to orient the scanner when we move it from one station to another so that the initial cloud alignment can be obtained. The aforementioned point cloud registration or auto-registration is achieved typically with a high degree of success without any user input or intervention into the Trimble specific software.
The technical specification of Trimble X7 laser scanner according to [41].
Scan Parameters | Trimble X7 Specifications |
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Range principle | High speed, digital time-of-flight distance measurement data |
Range noise | <2.5 mm @ 30 m |
Range | 0.6–80 m |
Field of view (degree) | 360° × 282° |
Scan speed | Up to 500 kHz |
Range accuracy | 2 mm |
Angular accuracy | 21″ |
3D point accuracy | 2.4 mm @ 10 m, 3.5 mm @ 20 m, 6.0 mm @ 40 m |
Scanning EDM laser class | Laser class 1, eye safe in accordance with IEC EN 60825-1 [42] |
Laser wavelength | 1550 nm, invisible |
Weight | 5.8 kg |
Dimensions | 178 mm (W) × 353 mm (H) × 170 mm (D) |
2.3. Three-Dimensional Modeling BIM Software
The process of 3D scanning modeling was performed using the Revit 2021 software, which encompassed the following stages:
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Data importation into Revit: After the processing of the raw data obtained from the scanning process, wherein errors and noise were eliminated, the 3D data were imported into Autodesk Revit. Revit is Building Information Modeling (BIM) software that is extensively used for the digital representation and management of building data. In Figure 2a, the 3D point cloud is shown as it appears after being imported into the program. This contains all the information about the building, such as the structural parts, partition walls, ventilation equipment, furniture, textile materials, and position of the windows. Additionally, the presence of undesirable substances, including dust, grime, and remnants of construction materials, was noted. In this specific case, as is observable in Figure 2b, these accumulations obstructed the precise identification of horizontal elements located directly on the floor.
Figure 2.
(a) 3D point cloud. (b) Identification of obstructed areas. (c) Section through the 3D scan depicts a two-dimensional representation of structural elements in red, and the representation of contextual elements (walls, windows, steps, and furniture) is made with black lines.Figure 2.
(a) 3D point cloud. (b) Identification of obstructed areas. (c) Section through the 3D scan depicts a two-dimensional representation of structural elements in red, and the representation of contextual elements (walls, windows, steps, and furniture) is made with black lines. -
Two- and three-dimensional modeling in Revit: The initial phase of modeling involved scrutinizing the scanned data to discern elements constituting the roof framing. Initial efforts included segmenting the scanned data to ascertain the profiles present within the framing structure. In Figure 2c, the identification of structural components is presented, followed by their representation in a two-dimensional format. The scan is shown in grayscale, with the red lines representing the identified contours of the structure. These profiles were initially drawn in a two-dimensional format, recognizing the existence of some unclear areas in the scan. Additionally, other blurred regions, as shown in Figure 3a,b, were later identified as ventilation ducts in the 3D modeling phase. In Figure 3c, the components constituting the truss beam are presented. These elements are as follows: bottom and top chord, webs, and gusset plates. Along with areas that could not be scanned, these are marked in red in Figure 4. The comprehensive 3D model was developed using the “Component/Model in-Place” tool in Revit, employing techniques such as Extrusion, Blend, and Sweep for this purpose [43].
Figure 3.
Detailed area: (a) representation in section—2D model. In this section the point cloud is colored green, yellow and purple, and the outline of the profiles is represented with red lines; (b) representation in axonometric view with 3D model; and (c) representation of a metallic profile in cross-section from a 3D point cloud.Figure 3.
Detailed area: (a) representation in section—2D model. In this section the point cloud is colored green, yellow and purple, and the outline of the profiles is represented with red lines; (b) representation in axonometric view with 3D model; and (c) representation of a metallic profile in cross-section from a 3D point cloud.Figure 4.
(a) The 3D model created in Revit® and the marking of elements inferred from the repetitiveness of the structure. (b) Virtual reconstruction of unscanned elements (shown in red), based on existing geometry.Figure 4.
(a) The 3D model created in Revit® and the marking of elements inferred from the repetitiveness of the structure. (b) Virtual reconstruction of unscanned elements (shown in red), based on existing geometry. -
Analysis: All structural elements were created using the “Structural Framing” feature, and the connecting elements were created with components categorized as “Structural Connections”. Elements identified based on the point cloud were assigned a material designated as “cloud”, while those created outside the point cloud were labeled “red”. Using these settings enabled the execution of a differentiated area calculation, distinguishing between elements identified from the scan and those created to complete the structure.
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Collaboration and sharing: In order to share the model with other colleagues, it was exported in the IFC (Industry Foundation Classes) and DWG (Drawing) formats. The 3D model created in Revit served as a basis for verifying the existing structure’s strength according to current standards. For this purpose, the openings and overall dimensions were required, rather than detailing all profiles. The detailing was to be added subsequently.
2.4. Methodology
The methodology used for this research is similar to that used in real practice. At the same time, a multidisciplinary team composed of surveyors, architects, and civil engineers contributed to this project, with each member of the team having a role and tasks adapted to his/her specialization.
Surveyors oversaw carrying out and processing the 3D scan for the architects. The architects were in charge of the 3D modeling, extracting the fireproof coated surface area from the BIM software and providing 2D drawings for the civil engineers. The civil engineers were in charge of the manual surveying of the roof structure, including the identification of steel profiles from standard profile catalogues and the manual calculation of the fireproof coated surface area, using the 2D drawings.
The first step consisted of surveying the existing steel truss structure of the roof, using traditional techniques and tools (measuring tape, caliper, paper, and pen), and also using 3D scanning technology. It was necessary to survey the structure partially manually, using traditional techniques, to complete the data collected by TLS because the configuration of the roof structure did not allow for the positioning of sufficient scanning stations to eliminate the shadowing effect, nor did it allow for the introduction of a UAV equipped with 3D scanning equipment. So, there is a risk that the data collected using only 3D scanning are insufficient to model the composite elements of steel trusses correctly. Thus, the traditionally collected data could also be used to check and correct the initial 3D model which was made exclusively from the point cloud generated by the 3D scan.
The second step consisted of the 3D modeling of the steel trusses, using exclusive data collected from the point cloud collected by TLS. This model was exported in .ifc format to be checked and later completed with data collected through a manual survey. After correcting the initial 3D model, the 2D drawings needed to manually calculate the coverage areas of the elements that required fireproofing were extracted and exported in .dwg format.
In the third step, the coverage areas of the profiles to be fireproofed were calculated. In order to check, analyze and evaluate how the structure survey technique and the area calculation technique influence the results, three areas were generated: one was automatically generated using the 3D model obtained with the help of 3D scanning, one was automatically generated from the 3D model obtained using manual surveying, and one was manually calculated using traditional quantity surveying techniques based on 2D drawings and standard profile catalogues.
The automatic generation of the areas was performed in ArchiCAD 26 by creating a custom Surface Schedule that was configured to automatically generate a list of coverage areas of the steel elements that had to be fireproofed. In this list, the elements were automatically grouped by element type, area subtotals were generated by element type to allow checking and identifying errors, and finally, the total fireproofing area was calculated.
The manual calculation consisted in the first phase of extracting from the 2D drawings the lengths of the elements that had to be fireproofed and calculating the total lengths for each element type. Subsequently, the coverage area (AL) of the profiles used in the project was extracted from the standard profile catalogues, and this area was multiplied by the previously calculated lengths. All of these calculations were performed in Google Sheets to be shared with all team members for verification and evaluation.
After the evaluation of these data, it becomes possible to identify the most efficient method of data acquisition, 3D modeling, and coverage area calculation of steel trusses, while ensuring construction waste reduction.
3. Results
The terrestrial laser scanning process consisted of mounting the Trimble X7 scanner in a total of 33 different scanning positions (stations), which covered the necessary data acquisition for both the roof section and the Aula Magna. Twelve of these stations were used to scan the roof area, yielding 340.341.222 points. The total scanning time from all 33 stations was just under an hour (58 min). This remarkable speed was achieved also due to the fact that this type of equipment does not need scanning targets (specific spherical or other types of targets) for the point cloud registration (assembling the whole 3D model resulting from different stations). The whole registration process is based on identifying common points in scans from different stations, with the maximum error for registration being 1.5 mm. Due to this, the uncertainty regarding the correct position of the scanned points is kept to a near minimum, with the reported average confidence level being around 97.6% and the rest being accounted as noise.
The 3D model made in Revit was exported in *.ifc format and imported into ArchiCAD to be checked by the team members who carried out the survey using traditional techniques. This model was also used to automatically calculate the areas that needed to be fireproofed.
After checking the 3D model obtained using only the information gathered by the 3D scanner, it was easy to notice that most of the steel elements were identified and modeled incorrectly because they had different sizes and/or sections compared to the real ones. However, an automatic calculation of the covering areas of the entire structure was carried out using the functions of ArchiCAD because we wanted to analyze and compare these values with those obtained using other calculation methods to identify the most efficient method to obtain the correct results, reduce waste, and also perfect a way of integrating the 3D scanning techniques into the design process.
Based on the calculations automatically performed in ArchiCAD on the exported model from Revit, the resulting surface area was found to be identical to that in Revit. The determined surface area requiring intumescent paint coverage was 609.30 square meters in both programs.
After performing this analysis, the 3D model was corrected in ArchiCAD by replacing the profiles that were identified incorrectly using the point cloud with the real profiles identified in the standard steel profile catalogues, using data collected through the traditional survey.
During the creation of the 3D model, the steel profiles were organized on different layers because we wanted the automatically calculated areas to be detailed by profile type to be able to analyze the results from different points of view, and also to more easily identify possible errors.
Based on the calculations made automatically by the BIM software on this 3D model, the surface that had to be covered with intumescent paint resulted in being 667.02 sqm. The actual area is therefore 57.72 sqm (9.47%) larger than the area automatically calculated by the BIM software using the 3D model made after the 3D scan. To validate these results, manual calculations were carried out. From this 3D model, the 2D drawings were extracted and exported in .dwg format so that the civil engineers could manually extract the lengths and sections of the profiles and then search for their coverage areas in the steel profile catalogues.
After analyzing these results, it can be seen that, by combining the data acquired by TLS with the traditional acquired data, an accurate 3D model can be produced in the BIM software, which facilitated both the automatic generation of accurate coverage areas and the production of the 2D drawings necessary for the accurate manual calculation of the areas.
In this case, the correct area is larger than the area calculated initially, and this means that the budgeted fireproofing cost and fireproofing materials ordered for this process would have been insufficient. In these situations, no material waste is generated because there is no surplus material that turns into waste, but non-physical waste is generated because of cost and time overruns. Cost overruns mean that the actual cost of fireproofing is higher than the approved budget for this work, and that an additional budget needs to be approved to complete the work. Time overruns are caused by the fact that, for public investments, approvals for budget supplements involve a time-consuming bureaucratic process, and fireproofing works should have been stopped until the necessary budget was approved.
4. Discussion
Terrestrial laser scanning (TLS) offers several advantages over traditional surveying methods when it comes to working time. Here are some key points highlighting how TLS can be more time-efficient compared to certain conventional surveying techniques:
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Rapid data acquisition and reduced field time: TLS can quickly capture a large amount of data in a relatively short period. Traditional surveying methods, such as manual measurements or total station surveys, may take significantly longer to cover the same area. This efficiency is particularly advantageous for projects with tight schedules. As was mentioned before, the whole scanning process took place in about 58 min, with this interval permitting the scanning of both the roof section and the Aula Magna Hall, with the timespan being significantly lower than that needed for traditional measurement techniques.
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Simultaneous data capture: TLS can capture data from multiple angles simultaneously, enabling a comprehensive view of the surveyed area in a single scan. This is in contrast to traditional methods, where each point or feature might need to be measured individually, leading to a more time-consuming process. Moreover, the scanner can capture plain 3D point clouds (as seen in Figure 9) but also colored details of the real-world environment, resulting in photorealistic 3D models (as can be observed in Figure 10b).
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Versatility in environments: TLS is highly versatile and well-suited for various environments, including complex or challenging terrains. Traditional survey methods may encounter difficulties in accessing certain areas or require additional time and effort to overcome obstacles. As can be observed in Figure 10b, the narrow beams and the void underneath the measured area would have made for a lengthy and unsafe operation for data acquisition using traditional methods.
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Real-time visualization combined with faster processing of data: TLS systems often provide real-time visualization of the scanned data. Surveyors can immediately assess the quality and coverage of the data, allowing for on-the-fly adjustments and ensuring that critical areas are adequately captured, without the need for rework. While post-processing is required for TLS data, advancements in software and processing algorithms have significantly reduced the time needed to generate usable results. Traditional survey methods may involve longer data processing times, especially for large datasets. The technique used in this application allowed for a rapid assessment of registration precision on the field, eliminating the need to check the precision afterwards and, in case of mismatches, to redo the data acquisition process, thus further enhancing the time and cost efficiency.
The existence of a 3D scan in the process of 3D modeling, particularly when using Autodesk Revit, has a significant impact on various aspects of the project. Below are some key areas where the impact is most notable:
Enhanced accuracy and detail: The 3D scanning process captures detailed and accurate measurements of the physical space or structure. When these data are imported into Revit, they provide a precise foundation for the modeling process, reducing the likelihood of errors that might occur when measurements are taken manually or estimated.
Identification of complex elements: The scanned data help in identifying complex structural elements, especially in intricate areas like roof framing. This level of detail aids in creating more accurate and functional 3D models, as seen with the identification and 2D rendering of specific profiles and the delineation of unclear areas.
Collaboration enhancement: The ability to export the model in IFC and DWG formats from Revit enhances collaboration. These formats are widely accepted and enable different stakeholders, even those using different software, to access, review, and collaborate on the project. The original 3D model made in Revit could be imported into ArchiCAD, using the .ifc format, without any problems. Thus, the 3D model could be easily corrected in a few hours in another program, by another person, because a large part of the 3D model could be kept, and only the elements that were initially wrongly identified were replaced.
Quantitative analysis and reporting: The precision of 3D scans can streamline quantitative analyses, such as quantity takeoffs. However, ambiguities in the scan can lead to errors in these reports, emphasizing the need for careful review and interpretation of the scanned data.
Automatic, fast, and accurate area calculation: Because BIM software has certain functions that automatically calculate the areas of different elements, it helped us to obtain the total areas in a few minutes after the completion of the modeling process. This aspect demonstrates that the use of BIM contributes significantly to easing the process of obtaining steel truss cover areas, as well as to obtaining more accurate bills of quantities. The fact that there is an insignificant difference of only 2.63 sqm (0.39%) between the area calculated automatically in the BIM software (667.02 sqm) and the area calculated manually (669.65 sqm) demonstrates that, by using BIM software, it is possible to obtain very accurate truss coverage areas automatically.
Miscalculation of the coverage areas of steel fireproofing trusses can produce waste in the following ways:
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When the calculated coverage area is larger than the actual coverage area, physical waste will result because excess material will be ordered and finally disposed of in the landfill.
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When the calculated coverage area is less than the actual coverage area, as could have happened in the case of the study presented in this paper, non-physical waste will result because not enough material is ordered, and therefore the budget allocated for fireproofing would not be sufficient. For public investments, the approval of the additional budget needed to complete the fireproofing works would have taken a long time and would have led to delays in the fireproofing works and ultimately to cost and time overruns.
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The steel truss elements described in this paper require a single coat of intumescent paint because the maximum coating thickness is 0.47 mm. It should be mentioned that, under certain conditions, depending on the cross-section of the profiles, fire resistance, and design temperature, the coating thickness may exceed 6 mm, and this involves applying the coating in multiple layers. Thus, the multiplication of coating layers leads to a multiplication of the waste produced.
5. Conclusions
The Aula Magna Hall at the University of Oradea, a key feature of the “SMART Campus—University of Oradea” project, showcases the university’s dedication to preserving heritage while embracing modernization and technological advancement.
The present project offers several pertinent conclusions and recommendations regarding the interpretation of point cloud scanning and its modeling using BIM software, such as Revit or ArchiCAD, in a harsh and highly complex environment. The main challenge that was identified for the automatic 3D modeling was the shadowing effect, which was caused by the fact that the laser wave cannot reach the narrow space between the profiles, and this can lead to the misinterpretation of the different sections of elements that have a cross-section composed of several profiles. The proposed solution recommends using multiple scanning stations and a combination of data acquisition methods, including traditional techniques and photography, overcoming the limitations of TLS scanning and generating precise and comprehensive 3D models for a better assessment of the construction site. Understanding the configuration of the steel truss is important, requiring site visits or visual materials for modelers in the absence of direct access. Calculation errors in composite trusses can lead to an underestimation of the materials needed for fireproofing, especially when multiple layers of intumescent paint are required. Thus, BIM programs become essential for efficient data collection and error prevention that could lead to waste.
Even though the initial automated modeling was generated inaccurately, this model created the necessary premises for verification of complex structural elements to be correctly identified. Thus, it resulted in the development of an innovative and accurate methodology so that a multidisciplinary team composed of surveyors, architects, and civil engineers are able the enhance their collaboration to generate an accurate and sustainable 3D model that is usable for multiple and highly complex analyses.
The research in this paper demonstrates that the use of 3D scanning and BIM contributes significantly to the reduction of non-physical waste that can result in the fireproofing process of steel trusses and helps yield a more precise budget, while taking into account the necessary recommendations presented in the article.
Author Contributions
Conceptualization, C.S., A.-H.P., N.-S.S. and S.N.; methodology, C.S., A.-H.P., I.-G.Z., A.-M.D., N.-S.S. and S.N.; software, A.-H.P., I.-G.Z., A.-P.F., A.S.B. and S.N.; validation, C.S., A.-P.F. and A.S.B.; formal analysis, C.S., A.-H.P., I.-G.Z., A.-M.D. and S.N.; investigation, C.S., A.-H.P., I.-G.Z., A.-M.D., A.-P.F., N.-S.S., A.S.B. and S.N.; resources, A.S.B.; data curation, N.-S.S.; writing—original draft preparation, C.S., A.-H.P., I.-G.Z., A.-M.D., A.-P.F., N.-S.S., A.S.B. and S.N.; writing—review and editing, C.S., N.-S.S., I.-G.Z. and S.N.; visualization, A.-P.F.; supervision, C.S., A.-H.P. and N.-S.S.; project administration, A.-H.P., I.-G.Z., A.-M.D. and A.-P.F.; funding acquisition, A.S.B. and S.N. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the University of Oradea.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restriction.
Acknowledgments
We gratefully acknowledge the anonymous reviewers and the editor for their thoughtful comments and suggestions, which contributed significantly to improving the quality of the manuscript. Also, we want to thank the local Trimble distributor, Liviu Gherman, and Sergiu Ceausu for all their support.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
(a) Building F—inner courtyard façade. (b) Building F—University Street façade.
Figure 1.
(a) Building F—inner courtyard façade. (b) Building F—University Street façade.
Figure 5.
Graphic illustration of the process followed to determine the areas needed to calculate the lists of quantities.
Figure 5.
Graphic illustration of the process followed to determine the areas needed to calculate the lists of quantities.
Figure 6.
Chart illustrating the modeled surfaces based on the point cloud (blue) and those completed for areas missing from the 3D scan (red).
Figure 6.
Chart illustrating the modeled surfaces based on the point cloud (blue) and those completed for areas missing from the 3D scan (red).
Figure 7.
In reality, all the profiles are spaced apart (a,d,f), but in the 3D model built after the 3D scan, the profiles were connected (c,e,g). (a) The actual section of the bottom chord of the truss composed of two spaced L-type profiles with equal angles (L90 × 90 × 10). The actual section has a coverage area of 0.702 m2/m. (b) Two connected L-type profiles. (c) The section of the bottom chord modeled after the 3D scan. (d) The actual section of the top chord (L100 × 100 × 12). (e) The section of the top chord modeled after the 3D scan. (f) The actual section of the webs (L65 × 65 × 7). (g) The section of the webs modeled after the 3D scan.
Figure 7.
In reality, all the profiles are spaced apart (a,d,f), but in the 3D model built after the 3D scan, the profiles were connected (c,e,g). (a) The actual section of the bottom chord of the truss composed of two spaced L-type profiles with equal angles (L90 × 90 × 10). The actual section has a coverage area of 0.702 m2/m. (b) Two connected L-type profiles. (c) The section of the bottom chord modeled after the 3D scan. (d) The actual section of the top chord (L100 × 100 × 12). (e) The section of the top chord modeled after the 3D scan. (f) The actual section of the webs (L65 × 65 × 7). (g) The section of the webs modeled after the 3D scan.
Figure 8.
(a) Axonometric drawing of the second 3D model made in ArchiCAD 26 software. (b) Axonometric drawing with a detail of the metal profile joints extracted from the 3D model made using only 3D scan data. (c) Axonometric drawing with a detail of the metal profile joints extracted from the 3D model made using 3D scan and traditional survey data. It can be seen that the elements composed of two metal profiles were modeled as a single profile.
Figure 8.
(a) Axonometric drawing of the second 3D model made in ArchiCAD 26 software. (b) Axonometric drawing with a detail of the metal profile joints extracted from the 3D model made using only 3D scan data. (c) Axonometric drawing with a detail of the metal profile joints extracted from the 3D model made using 3D scan and traditional survey data. It can be seen that the elements composed of two metal profiles were modeled as a single profile.
Figure 9.
The black points represent the point cloud from the 3D scan. With blue are represented the steel truss elements that could be modeled from the point cloud, while with red are highlighted the elements that were modelled by deduction.
Figure 9.
The black points represent the point cloud from the 3D scan. With blue are represented the steel truss elements that could be modeled from the point cloud, while with red are highlighted the elements that were modelled by deduction.
Figure 10.
Detailed area. (a) Representation of a metallic profile in cross-section from a 3D point cloud. (b) Overview photo of the spatial structure.
Figure 10.
Detailed area. (a) Representation of a metallic profile in cross-section from a 3D point cloud. (b) Overview photo of the spatial structure.
Figure 11.
Graphic illustration of the proposed methodology for quantity surveying using 3D scanning, traditional surveying techniques, and additional data (hand sketches, photos, and videos).
Figure 11.
Graphic illustration of the proposed methodology for quantity surveying using 3D scanning, traditional surveying techniques, and additional data (hand sketches, photos, and videos).
Table 2.
Cross-section and coverage areas of real profiles and 3D Scan Model profiles.
Table 2.
Cross-section and coverage areas of real profiles and 3D Scan Model profiles.
Truss Element | Actual Section | 3D Model Section | Actual Coverage Area AL (m2/m) |
3D Model Coverage Area AL (m2/m) |
Difference |
---|---|---|---|---|---|
Bottom chord | 2 × 90 × 90 × 10 | 194 × 93 × 29 | 0.702 | 0.574 | +22.3% |
Top chord | 2 × 100 × 100 × 12 | 192 × 101 × 27 | 0.780 | 0.586 | +33.11% |
Webs | 2 × 65 × 65 × 7 | 124 × 59 × 20 | 0.504 | 0.378 | +33.33% |
Table 3.
Table centralizing the results obtained. The last two columns show the differences between the area calculated using the initial 3D model that was made using the 3D scanning survey and the area calculated using the second 3D model that was made via the manual survey.
Table 3.
Table centralizing the results obtained. The last two columns show the differences between the area calculated using the initial 3D model that was made using the 3D scanning survey and the area calculated using the second 3D model that was made via the manual survey.
3D Model Used for Calculation | Area Calculation Method | Calculated Area | Difference (sqm) | Difference (%) |
---|---|---|---|---|
3D scanning survey 3D model |
Automatically calculated area | 609.30 sqm | – | – |
3D scanning + manual survey 3D model |
Automatically calculated Area | 667.02 sqm | 57.72 sqm | +9.47% |
Manually calculated area | 669.65 sqm | 60.35 sqm | +9.90% |
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