=Paper=
{{Paper
|id=Vol-3266/paper9
|storemode=property
|title=BIM-FM integrated solution resourcing to digital techniques
|pdfUrl=https://ceur-ws.org/Vol-3266/paper9.pdf
|volume=Vol-3266
|authors=Raquel Matos,Hugo Rodrigues,Aníbal Costa,Fernanda Rodrigues
|dblpUrl=https://dblp.org/rec/conf/viperc/Matos0CR22
}}
==BIM-FM integrated solution resourcing to digital techniques==
https://ceur-ws.org/Vol-3266/paper9.pdf
BIM-FM integrated solution resourcing to digital techniques
Raquel Matos 1, Hugo Rodrigues 2 , Aníbal Costa 3 and Fernanda Rodrigues 4
1
RISCO, Department of civil engineering, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-
193, Portugal
2
RISCO, Department of civil engineering, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-
193, Portugal
3
RISCO, Department of civil engineering, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-
193, Portugal
4
RISCO, Department of civil engineering, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-
193, Portugal
Abstract
The Facility Management has been suffering significant changes since the introduction of BIM
in the AECO sector. However, there are still challenges in BIM implementation during the
building use phase, such as the difficulties related to the personalization of the maintenance
management information for each case, and modelling the as-built conditions. Disclosing
building data to all the stakeholders involved in the building life cycle is also a challenging
task. So, this paper aims to present an integrated solution for BIM-FM to categorize and
prioritize maintenance management, with the resource to digital techniques. For this purpose,
the methodology developed consists of 1- The recognition and preparation of the site
conditions; 2- Image collection using a UAV; 3 – Image processing; 4 – Obtaining point cloud
and its integration in a BIM software; 5 – 3D building modelling in Revit soft-ware, and 6 –
Placement of building anomalies by means of placeholders. This work allows exploring BIM
– FM representation and data integration for building condition assessment and its
representation in a collaborative tool. This methodology will enhance the usability of BIM
methodology in FM since it allows the data update in the model, avoids the information loss
or fragmentation of the building life cycle and gives access to BIM users and non-users.
Keywords 1
Facility Management, BIM, Maintenance Management, Photogrammetry, BIM-FM.
1. Introduction
The Architecture, Engineering, Construction and Operation (AECO) sector is facing several
challenges related to the current ageing of the building stock, and the impacts of COVID-19 which
required fast building adaptations [1] [2] and answers to the paradox of cost-reduction and quality
increase [3]. Besides that, the AECO sector is responsible for several negative impacts on the
environment, being under big pressure to contribute to carbon neutrality [4] [5] [6] [7].
Digitalization is considered by [8] as the answer to overcoming those challenges, but also, to
increasing productivity, efficiency, sustainability, safety, quality levels and innovation.
Building Information Modelling (BIM) is recognized as being one of the developments that have
been contributing most significantly to the digitalization of the AECO sector [8]. BIM is a collaborative
methodology that has been pushing the AECO sector for the shared digital representation of the
facilities, for the adequate building data sharing and storage between all the professionals involved in
the building life cycle [9]. However, the difficulties related to the representation of the existing
condition of buildings, the personalization of the information of maintenance management for each
VIPERC2022, 1st International Virtual Conference on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, 12
September 2022
EMAIL: rvpm@ua.pt (A. 1); hrodrigues@ua.pt (A. 2); agc@ua.pt (A. 3); mfrodrigues@ua.pt (A. 4)
ORCID: 0000-0002-0171-7842 (A. 1); 0000-0003-1373-4540 (A. 2); 0000-0001-8950-4843 (A. 3); 0000-0001-9127-7766 (A.4)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Wor
Pr
ks
hop
oceedi
ngs
ht
I
tp:
//
ceur
-
SSN1613-
ws
.or
0073
g
CEUR Workshop Proceedings (CEUR-WS.org)
case, as well as, the access of the BIM data for all the stake-holders involved in Facility Management
(FM) are still barriers to the implementation of this methodology [9].
For this purpose, an integrated solution for BIM-FM is proposed, to categorize and prioritize the
maintenance management, with the resource of digital techniques. In this proposal, the photogrammetry
technique is applied, to get the building point cloud and geometry, and facilitate the 3D building
modelling in Revit software. Then, the building anomalies were evaluated and quantified employing
the Method for Condition Assessment of Immobile Constructed Assets presented in CEN 17385:2019
[10], and represented by means of placeholders in the BIM model.
The placeholders are a family of objects, represented by a simple square geometry with parametric
information about anomaly type, its severity and % of affectation of the anomaly. Besides that, the
cracks were also geometrically represented in the model.
The following chapter presents the methodology developed and then described.
2. Methodology
A methodology to reach an integrated solution for BIM-FM with the resource to digital techniques
was developed, according to the following steps:
1. Recognition and Preparation of the site conditions.
2. Image collection using an Unmanned Aerial Vehicle (UAV) for photogrammetry purposes.
3. Image processing.
4. Obtaining point cloud and its integration in the Revit software.
5. 3D building modelling in Revit software.
6. Placement of building anomalies by means of placeholders and geometric representation of
cracks.
3. Case study
The methodology was developed and applied to a case study to check its future implementation. The
case study is from the Geosciences Department of the University of Aveiro, in Aveiro, Portugal,
authored by Architect Souto Moura. The building is composed of 4 floors. The basement is a storage
room. The first and second floors are composed of classrooms and laboratories. The third floor is
composed of professor offices.
The coatings are different from the traditional coatings used in the rest of the University building
campus. The Northeast and southwest facades of the Geosciences Department of the University of
Aveiro are composed of a concrete structures at the sight. Northwest and Southeast facades are
composed of walls of concrete at sight that supports the glazed openings in all the extensions of those
facades. These facades comprise a shading breezes system of pink marble supported by a metallic
structure fixed to the concrete structure.
The department has a rectangular geometry with two entrances in the Northeast and Southwest
facades. Figures 1 and 2 present the building under study.
Figure 1: Geosciences department of Aveiro's Figure 2: Geosciences department of
University - South façade Aveiro's University - South façade
Afterwards, the building digitalization was the next step, which was supported by the
Photogrammetry technique to obtain the building point cloud. For this process, a set of steps were
considered, as presented in Figure 3.
Figure 3: Steps to the digital processing
3.1. Recognition and Preparation of the site conditions
The first step consisted of the building characterization, in which the materials were classified and
the anomalies were registered. Besides that, the surrounding environment of the building was evaluated.
After the building and site conditions characterization, it was possible to verify that the building
understudy would have surfaces that could cause interferences in the photogrammetry technique to be
applied, such as:
• clear / transparent objects (glass).
• very thin objects that contain lots of details (shadow systems that carry out some details).
• Building geometry is very regular and repetitive.
The presence of these elements with undesirable properties can cause a very sparse point cloud/lower
quality of the point cloud and thus lower quality of the final model or problems with alignment.
Besides that, there was no need to use targets, albeit the regular geometry and similar building pattern.
3.2. Image collection by means of UAV for photogrammetry purposes
The photos were shot with an UAV, a so-called drone, and a Digital Single-Lens Reflex (DSLR)
camera. The specifications of these products are described in Table 1 and presented in Figure 4.
Table 1
Drone DJI MAVIC MINI specifications
DJI MAVI MINI Feature
Weight 249 g
Flight Time 30-min Max.
HD Video Transmission 4 km
Vision sensor and a GPS Precise Hover
3-Axis Gimbal
2.7K Camera
Figure 4: Drone DJI MAVIC MINI
Mavic Mini supports 12 MP aerial photos and 2.7K Quad HD videos. A 3-axis motorized gimbal
provides superior camera stability and ensures clear ultra-smooth footage. The MAVIC MINI drone
was used because it was the equipment available from the University, however, it is considered to be
one of the best pieces of equipment for the desired purpose in terms of cost-effectiveness.
The photos were taken on a sunny day due to the availability of the drone. How-ever, the photos
were shot in the middle of the day to avoid non-desired shadows. Furthermore, the vegetation around
the building caused sharp shadows on photos and thus problems with alignment and colours that do not
correspond to reality.
The drone got 3 rounds to the building at different altitudes from the ground:
Height 1=4.5 meters
Height 2=14 meters
Height 3=19 meters (roof)
The camera inclination was 20 degrees down and it took photos all around the building, at least with
60% of overlap.
Since the drone could not fly next to the ground, a set of images were taken to complement and
complete the model. For this purpose, a DSLR camera Sony Alpha A6100 with a lens of 16-50 mm
f/3.5-5.6., was used.
3.3. Image processing
After the building photos were gathered, the image processing in the software took place. The images
taken were evaluated in terms of their quality and the repeated images were deleted. The following step
was image processing.
The software for processing the images and creating and manipulating the point cloud was:
• Agisoft Metashape
• AutoDesk Recap PRO
• AutoDesk Revit
Regarding the hardware, after several attempts in other devices, a successful attempt was made by
a device with the following specifications (Table 2):
Table 2
Hardware features [11]
Specifications Recommended configurations Configurations used
Intel core i7 or AMD Ryzen 7 AMD Ryzen 9 5900X 12-core
Processor
Processor Processor 3.69 GHz
Installed RAM 32 GB 32 GB
64-bit operating system x64-
System type
based processor
Discrete NVIDIA or AMD GPU NVIDIA RTX3070
Graphic card
(4+ GB VRAM)
Windows 7 SP 1 or later Windows 10 Enterprise
Windows specifications
Version 1909
TB SSD, 1TB HDD
It is recommended to make sure that the Graphics Processing Unit (GPU) device is detected by the
program.
The photos were uploaded on Agisoft Metashape, which is a software that performs
photogrammetric processing of digital images. In this software, the process was done according to
Figure 5.
Figure 5: Steps of processing images in Agisoft Metashape
3.4. Obtaining point cloud and its integration in the Revit software
After the photos processing in Metashape according to Figure 5, it is possible to obtain the building
point cloud with the geometric building information (Figures 6 and 7). The Point Cloud was exported
from Metashape by the format .e57, which is not a compatible format to import to Revit. So, the file
.e57 was transformed to .rcp by Autodesk Recap Pro and then imported to Autodesk Revit.
Figure 6: Point cloud in building texture – NE façade in Metashape
Figure 7: Point cloud in building texture – SE façade in Metashape
3.5. 3D building modelling in Revit software
After being imported to Autodesk Revit, the building geometry was modelled in overlap with the
point cloud, which facilitated the modelling process, becoming a swift and prompt process (Figure 8).
Figure 8: Steps of processing images in Agisoft Metashape
In the building modelling, the concrete facades at sight were modelled, as well as the customized
glass openings. The shading breezes system and the solar panels were modelled as families of objects
to customize the dimensions, materials and respective support. Figures 9 and 10 present the building
modelling.
Figure 9: Building Modelling in Autodesk Revit
Figure 10: Building Modelling in Autodesk Revit – roof plan
3.6. Placement of building anomalies by means of placeholders.
Being the 3D building modelling in a BIM environment, more data was added to the model.
Figure 6 represents the point cloud with texture, by which is also possible to identify the place of
the existing building anomalies. This representation facilitates the representation of the anomalies in
the BIM model employing placeholders and crack families.
So, by the previous building inspection done, the Method for Condition Assessment of Immobile
Constructed Assets presented in CEN 17385:2019 was applied. This method takes the defect severity,
the level of degradation, and the extent of the defect into account, which results in the condition classes.
The defect severity is related to the influence of the defect on the performance of the element. The level
of degradation is related to the visible physical condition. The extent of the defect assesses the area or
volume of the element that is affected. The condition class is the result of merging the severity, level of
degradation and extent of defect, and represents the condition of the element.
Based on this method, a family of placeholders with parametric information about the anomaly ID,
severity of the defect and the extent of the defect was developed (Figure 11).
Figure 11: Parametric information linked to Placeholder family
The placeholders were placed in the building model according to their condition class, the poor
condition represented by red squares, the fair condition represented by the yellow square and the good
condition represented by the green squares.
Figures 12 and 13 present the Northeast face of real photography and the 3D modelling with the
placeholders, being the NE facade evaluated with a poor condition and represented with the red
placeholders since the concrete façade has wide-spread anomalies. Besides that, cracks were also
represented in the model by means of a family.
Figure 14 presents the complete building model with the placeholders translating the elements’
conditions, which allows for prioritizing maintenance actions.
Figure 12: Northeast façade - photography Figure 13: Northeast façade – 3D model with
place-holders and cracks
Figure 14: Building model in Revit with placeholders and cracks
4. Conclusions
The operation and maintenance phase is the longest and the most expensive phase of the building
life cycle, which has been adding importance to FM, especially since the digitalization of the AECO
sector. BIM and other digital techniques like photogrammetry, and laser scanning, make FM gain more
focus.
However, there are still some barriers to the implementation of them in FM, like the customization
of maintenance management information for each case as well as the disclosure of the building life
cycle information. So, this work presents a methodology that allows having a BIM-FM integrated
solution for building condition assessment and its representation in a BIM environment, which is a
collaborative methodology and a repository of building life cycle data. For this purpose, making use of
the photogrammetry techniques, the building was modelled in Revit. And then, the placeholders and
crack families were placed in the model, representing the building defects. The methodology developed,
makes it possible for the systems condition assessment, its classification and representation in the 3D
model, and consequently, maintenance actions prioritization and management. This methodology
leverages BIM methodology in FM since it is a quick method to include building maintenance data in
the model, allowing its easy update, which makes it possible to avoid the information loss or
fragmentation of the building life cycle. So, this study intends to contribute to more efficient
coordination of the maintenance management activities, which are under development.
As for future developments, it is expected to automatize the recognition and classification of
degradation in historical buildings to finally interpolate this geometric and numerical information with
a BIM methodology, obtaining a representation and quantification of the information adapted to the FM
processes.
5. Acknowledgements
This research work was partially funded by the Portuguese Government through the FCT
(Foundation for Science and Technology) and European Social Fund under the PhD grant
SFRH/BD/147532/2019, awarded to the first author.
6. References
[1] RICS, “COVID-19 and its effect on facilities management,”, [Online]. Available:
https://www.rics.org/uk/products/data-products/insights/covid-19-and-its-effect-on-facilities-management/, last accesses
2021/12/31.
[2] M. Awada et al., “Ten questions concerning occupant health in buildings during normal operations and extreme
events including the COVID-19 pandemic,” Build. Environ., vol. 188, no. September 2020, doi:
10.1016/j.buildenv.2020.107480, (2021).
[3] F. Matos, R., Rodrigues, H., Costa, A., Rodrigues, “Building Condition Indicators Analysis for BIM-FM
Integration,” Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-022-09719-6 (2022).
[4] United Nations -“Transforming our World: The 2030 Agenda for Sustainable Development.” (2015).
[5] European Commission, “Building sustainability performance - levels,” Build. Sustain. Perform,
http://ec.europa.eu/environment/eussd/buildings.ht, doi: 10.2779/562960, (2017).
[6] P. Benítez, F. Rodrigues, S. Talukdar, S. Gavilán, H. Varum, and E. Spacone, “Analysis of correlation between real
degradation data and a carbonation model for concrete structures,” Cem. Concr. Compos., vol. 95, no. October 2018, pp. 247–
259, doi: 10.1016/j.cemconcomp.2018.09.019, (2019).
[7] IEA, “International Energy Agency - Buildings A source of enormous untapped efficiency potential.” 2020,
[Online]. Available: https://www.iea.org/. last accesses 2021/12/31.
[8] R. Rodrigues, F., Alves, A., Matos, “Construction Management Supported by BIM and Business Intelligence Tool,”,
Energies 2022, 15, 3412. https://doi.org/10.3390/ en15093412, (2022).
[9] J. V. Moreno, R. Machete, A. Paula, A. B. Gonçalves, and R. Bento, “Dynamic Data Feeding into BIM for Facility
Management : A Prototype Application to a University Building,” Buildings 2022, 12, 645. https://
doi.org/10.3390/buildings12050645, (2022).
[10] CEN / TS 17385, “Method for Condition Assessment of Immobile Constructed Assets,” no. November, pp. 1–6,
(2019).
[11] Agisoft Metashape “Agisoft Metashape User Manual,” Agisoft Metashape, no. September, p. 160, 2019, [Online].
Available: https://www.agisoft.com/pdf/metashape-pro_1_5_en.pdf. last accesses 2021/12/31.