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BIM Application for Automated Solutions


Construction industry is behind the advanced technology compared to the other sectors. While many industries improved rapidly with automatized solutions, construction industry could not reach the same development level due to fact that it has fragmented structure and all project requires its own unique solutions. All companies have their own specific library and data classification; therefore, data transition is resulted with loss of information, incorrect estimation and inefficient project management. In that phase, Building Information Modeling (BIM) takes a step forward with standards and data classification. Converting to project data into computable environment contributes to create useful information and benefit from it in communication between different professions and architecture. This study emphasizes that mutual information exchange between BIM and construction site. Moreover, it investigates the technologies which direct the construction sector to advanced automatized solutions.


Digitalization in construction is starting with 2D drawing and continues with BIM solutions. Converting the architectural drawing to BIM project creates exchangeable information between the disciplines which provides different types of data source. Data collection from other disciplines reveals the filtering to convert data to useful information for benefitting from it. Object oriented modeling method behind BIM benefits from the sources and contributes to semantic filtering according to project requirements. The data from the physical world from various disciplines is filtered and transferred to the virtual environment to simulate and optimize the process and create a faster production system (Zhang et al., 2022). In parallel with other industry especially automotive sector, the robotic technologies with semantic data, point cloud and others decrease the deviations between “as-planned” and “as-built” projects. Visualization and digital twin ensure a more predictable project management process and risk management. Environmental data collection during the design process decreases the failures, cost and time consumption related to failures. Unmanned Aerial Vehicles (UAV) provides the data with laser scanning, point cloud and with other sensors to create communication between construction site and digital environment. Additionally, Artificial Intelligence (AI) is a tool for management to follow workflow and project the phases. BIM generates a transparent and predictable process compared to common practice which could be resulted with failures due to lack of BIM Management. The study focuses on mutual information exchange between construction sites and BIM, additionally the technologies behind it.


Building Information Modelling is an information management system in construction industry. It archieves the information semantically with family and data classification. In contrast to current solution, it is object oriented and parametric modeling. Moreover, large scale projects contain too many stakeholders, BIM standards ISO 19650 systematized the information management. (Seyis, S., Çekin, E., 2020) Not only the standard but also format; Industrial Foundation Class (IFC) is the readable format in all BIM tools to prevent loss of information during data transmission. IFC working with daha schema provides semantic information of project with relationship and entities (Karimi et al., 2021). It allows to manage construction project phase by phase from the top on down. There are many opportunities for BIM for the project. This article focuses on 3 project to reveal data transmission in; Robotic Rebar Cage Production, Photometry, Point Cloud and Augmented Reality.

2.2.Rebar Cage

Case 1 | Data from BIM to Production

In the construction site, installation of rebar is time-consuming and requires effort because of its weight. On one hand, installation affects time scheduling directly and project management, on the other hand, the failures cause additional cost and conflict between construction and BIM data. The research purpose is minimizing the conflict and automatized rebar cage installation based on BIM to overcome the scheduling problems. The process experience with CoppeliaSim provided by Skanska.

Abb.02 Rebar Cage Production System (Momeni et al., 2022)

2.2.1. Data | Methodology

3D model of the rebar cage based on BIM used as a main source. 3 types of rebar categorized, rebar dimension, rebar’s placement location , information describe in BIM model. Installation order, place paths and tie instruction, homing position designed based on BIM model and project requirement. To cooperate robot arm and BIM in real-world, installation order, potential grip points calculation are essential. All instance described in a frame which based on cartesian coordinate system. To control robot, movements described based on BIM reference frame (Momeni et al., 2022). Picking the most compatible movement from provided movements by robot is time-consuming and requires too many experiments. However, this method could be used for a long time to product standard rebar cage. Each rebar cage requires a different approach path in the current stage. Nevertheless, path optimization could be automated with other solutions.

Abb.03 Flowchart (Momeni et al., 2022)

2.2.3.Achievement and Assumption

The process based on assumptions like;

- Gantry is rigid structure, it does not twist or bend

-Robot joints do not affected by movement

-Rebars are in ideal size and weight and there are no deflections. (Momeni et al., 2022).

Simulations meet the expectations under the provided circumstances. However, the data transmission is one sided in the project which caused the failures. To manage rebars installation considering deflection failures, AI is the best solution. In this way, preventing the unexpected faults decreases the additional cost, time consumption and having “as-built” data contribute to the facilities management.

Abb.04 BIM Model (Momeni et al., 2022)


Case 2 |Data from site to BIM

Drones offer advantages for data collection especially in inaccessible and risky places. While It reduces the labors and equipments need, provides construction safety. Collected data converts into information input with photometry which provides real world information to the BIM. It is very widely used for feasibility and field studies or analyzing the existing buildings. Bringing data from the real world to the visual environment shapes future projects and reduces the inaccuracy. While facility management is more accessible in buildings with BIM data, current studies investigate production of digital twins for existing buildings which contributes data to the maintenance industry. Within this scope, related project will be defined to show wide range of photometry and BIM collaboration. Afterwards, auto labeling projects including different approaches will be discussed.

2.3.1.Related Projects

There are too many companies and research groups which investigates digital data for construction management, real world communication

Abb.05 TwinGen (

TwinGen Project from RWTH Aachen University studies data collection from existing infrastructural buildings with photometry and laser scanning. Creating a data set and converting it to German semantically ontological data model for inspection in existing buildings gives room for an automated maintenance solution based on BIM. (Göbels, A., Beetz, J., 2021)

Abb.06 Photometry Application (

Second project, skycatch data imaging company provides solution for mining and construction industry. Drone system which controls the workflow with point cloud and photometry, visualize and calculate the current and further stage without engineer in site. Within this technology, schedules, additional requirement, completed task projected in BIM environment. (Firth, N., 2018)

Abb.07 Flowchart (Braun, A., Borrman, A., 2019)

Lastly, According to Braun and Borrman (2019), Semantic data extracted from BIM provides an opportunity for auto labeling to components in construction sites. Machine Learning approach is proposed to classify the object in the real world for comparing BIM 4D and application. It is expected to distinguish the differences with overlapping the BIM 4D and ML. In this case, project scheduling will be monitored for management.

Abb.08 Drones System (Braun, A., Borrman, A., 2019)

2.3.2.Data | Methodology

Research aim is to compare BIM 4D data and trained drone images with Convolutional Neural Network (CNNs) and prevent delay in project. BIM contains semantic data which allows to pick each component in the system. With 4D, time, and information it is possible to compare as-built and as-planned projects. Author proposed Machine Learning to collect data from construction sites. However, CNNs requires a huge amount of labeled data set for each element and the process is time consuming.

Abb.09 Labeled Column (Braun, A., Borrman, A., 2019)

Braun and Borrman (2019), proposed to use BIM data for labeling the instance such as column, wall. Moreover, BIM contributed to time planning with components and extracting it in JSON format. Collected real-time images from drones are overlapped with labeled BIM 4D output. Despite the fact that this method does not use machine learning to observe real time processes in the current stage, the output provides communication between BIM and real world applications.

Abb.10 Semanctic System (Braun, A., Borrman, A., 2019)

2.4.Augmented Reality

Data from BIM to site

Because of the agreement between the parties, visualization tools and softwares is commonly used to avoid delays. Extended reality, containing AR, are demanded in the site to see project development. Considering common failures based on deficiencies of communication with the construction site, AR is addressed as one of the solutions. There are several variations in this fields such as; VR, AR, mixed environment, smart glasses etc. The paper takes a look into XYZ company with AR and smartglasses solutions.

2.4.1.Data | Methodology

BIM projects with MEP and 4D are transferred into construction sites to prevent collision and deviation. This integration decreases real-world problems during installation and estimation. (Cvetković, D., (Ed.). 2022). There are many ways to improve AR-BIM integration and challenges like differences in the origin of the tracking system. To ensure organized data processing for the final output, firstly, geospatial properties should be generated for BIM objects. Secondly, from the moment users location and points in BIM need to be identified. Lastly, to use BIM in AR, geometrical data and property data should be separated. (Williams et al. 2015)

Data transition needs to be defined with rules to organize efficient workflow with BIM also with AR. Once the system is set up, AR can be used efficiently to visualize the BIM application in the real world. Commonly, BIM softwares detects collisions and visualizes workflow. Augmented reality contributes various facilities together in the real world. XYZ provides information on construction sites about instructional application, project management, and installation instruction for mechanical systems. (Reality, n.d.)

Abb.11 AR Application (


Many researchers investigate data transmission in the BIM process. Because of the fact that the BIM environment visualizes the project and is used as a tool. To achieve project “as-planned” and contribute to the facility management environmental factors and unpredicted conditions should be considered. Data from external sources improved the project. BIM creates semantically data classification. Moreover, it ensures the possibility of automated solutions with computable and transformable information which contribute to the building in all phases of cycle from design to demolition.

What could be the approach in further steps? Computable information enables architecture communication with other engineering disciplines. Based on this, artificial intelligence could be a key point for data collection from the real world. Rebar cage production failed due to assumption and lack of real world information. Even though Auto labeling using ML data research failed due to the complex structure of the construction project. In my point of view, starting with more definable instances and simplifying the process would increase the accuracy in machine learning.



Zhang, J., Luo, H., & Xu, J. (2022). Towards fully BIM-enabled building automation and robotics: A perspective of lifecycle information flow. Computers in Industry, 135, 103570.

Conference Paper

Çekin, E., Seyis, S. (2020). BIM Execution Plan based on BS EN ISO 19650‐1 and BS EN ISO 19650‐2 Standards. 6th International Project and Construction Management Conference, Istanbul, Turkey Retrieved from


Karimi, S., Braga, R. G., Iordanova, I., & St-Onge, D. (2021, April 20). Semantic navigation using building ınformation on construction sites.


Momeni, M., Relefors, J., Khatry, A., Pettersson, L., Papadopoulos, A. V., & Nolte, T. (2022). Automated fabrication of reinforcement cages using a robotized production cell. Automation in Construction, 133, 103990.

Conference Paper

Göbels, A., & Beetz, J. (2021, October 10). Conversion of legacy domain models into ontologies for infrastructure maintenance. LDAC 2021 - 9th Linked Data in Architecture and Construction Workshop, Luxembourg


Firth, N. (2018, March 14). AI drones are controlling self-driving diggers on building sites. New Scientist.


Braun, A., Borrmann, A. (2019). Combining inverse photogrammetry and BIM for automated labeling of construction site images for machine learning. Automation in Construction, 106, 102879.

Book Chapter

Cvetković, D. , (Ed.). (2022). Augmented Reality and Its Application. IntechOpen. Retrieved from June 26, 2021,


Williams G, Gheisari M, Asce A.M., Chen P-J, Irizarry J, Asce M. Mint, (2015) BIM2MAR: An Efficient BIM Translation to Mobile Augmented Reality Applications. Journal of Management in Engineering. 31:1: 1-8. DOI: 10.1061/(ASCE)ME.1943-5479.0000315.


Chen, J., Yi, J. S. K., Kahoush, M., Cho, E. S., & Cho, Y. K. (2020). Point cloud scene completion of obstructed building facades with generative adversarial ınpainting. Sensors, 20(18), 5029.


Reality, X. (n.d.). Home. Retrieved March 19, 2022, from

List of Figures

Abb.01 Cover

Abb.02 Rebar Cage Production System (Momeni et al., 2022)

Abb.03 Flowchart (Momeni et al., 2022)

Abb.04 BIM Model (Momeni et al., 2022)

Abb.05 TwinGen

Abb.06 Photometry Application

Abb.07 Flowchart (Braun, A., Borrman, A., 2019)

Abb.08 Drones System (Braun, A., Borrman, A., 2019)

Abb.09 Labeled Column (Braun, A., Borrman, A., 2019)

Abb.10 Semanctic System (Braun, A., Borrman, A., 2019)

Abb.11 AR Application (

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