Systematization of BIM data management to improve centralization,interoperability and traceability of building asset information in digital environments

Abstract

This article connects Building Information Modeling (BIM) methodology with relational databases, presenting a large-scale framework that enhances work processes and knowledge for digital building asset management. It emphasizes the significance of data connections, aligning digital technologies with valuable information extraction and advocating for strategic digital continuity. Current digital procedures in construction often lack standardized protocols, hindering efficient information creation, monitoring, and reuse. The integration of BIM with other data sources facilitates better control in coordination and management, leading to time and cost savings throughout the asset lifecycle. The research is organized into four blocks: database construction, BIM data integration, quality control, and data consolidation, leading to a new systematization proposal for building management. Key contributions include insights into the global structuring of asset management information and the potential for synchronous use of BIM models with relational databases, enabling data sharing and access for various specialists across different environments.

Keywords

BIM data management IFC interoperability

Introduction

Over the last decades, digitalization and the emergence of new technologies in the AECO sector has allowed construction professionals to obtain different benefits, such as saving time during project development, increasing productivity, simplifying work methods or improving the quality of documents.^1,2 These innovative technologies and work procedures are gradually breaking down information silos and allowing digital continuity through multiple initiatives and new disruptive approaches, which guarantees that information remains coherent, reliable and unique as it is generated and updated by specialists.^3,4

The use of the Building Information Modeling (BIM) methodology in construction projects has improved the integration of processes and coordination between all participants as it is based on better collaboration and optimal information exchange within a common data environment, although this entails a complex implementation study that requires appropriate strategies that allow incorporating all the activities and processes of the asset.^5–9 This is added to different barriers that exist in the sector that prevent successful adoption of the methodology, such as lack a of knowledge of BIM within the sector, the complexity of managing contracting processes, technological factors, financial challenges or new legal implications.^10–12

Since its inception, the BuildingSmart association has promoted the application of the BIM methodology, as well as the use of the Industry Foundation Classes (IFC) standard, which establishes a standardization and classification of construction information models. Different research has been carried out in recent years to integrate new data schemes and relationships within the standard, related to analysis and energy efficiency, automation in object classification processes, BIM-GIS data integrations or definitions in construction time management.^13–17 However, there are different limitations in working with IFC files.¹⁸ Since not all software allows working with all the resources, relationships and entities contained in the standard, it is not always possible to exchange all the necessary information between multidisciplinary teams and not all construction elements are modeled within the virtual model.

Background

The construction process of the digital building asset begins with an order placed by the client or owner to an external provider, designating a service or work in which a series of activities must be planned, managed and executed to meet the needs and client objectives. In the course of these activities, the definition of the information requirements from the initial stage is essential, as well as the continuous monitoring of the data and data change management in all phases of the project.^19–21

Otherwise, the ISO 19650 standard, relating to the management of information throughout the life cycle of a built asset, establishes the definition of Level of Information Need (LOIN) to determine the quantity, quality, granularity and detail of the asset data. This concept is developed in the ISO 7817 standard, relating to Building Information Modeling - Level of information need, which indicates that each set of data in each phase must be defined based on its objectives, its function and its purposes, establishing a clear distinction between geometric elements, alphanumeric information and associated documentation. All of this improves the previously existing concept of Level of Development (LOD), since the focus on information management is changed, giving greater importance to data (Figure 1) and generating a dynamic scheme in which all parties must get involved.²²

Figure 1.

Alphanumeric data types in building environments.

The project’s information requirements must be reviewed and approved by all parties before work begins. This ensures monitoring of data compliance with agreed requirements at each milestone.^23–25 Automation mechanisms can streamline regulatory compliance and manage data types, units, geometries, materials, and other definitions (Figure 1).^26–29 BIM models also allow data extraction for analysis, with templates accelerating processes and ensuring information exchange across platforms.^30,31

After defining and integrating asset data into discipline-specific models, a federation of models forms a global project database, allowing detection of multidisciplinary issues and ensuring data consistency.³² In this way, data from different sources is shared, verifying coherence, and establishing semantic associations.^33,34 Some studies highlight using programming languages like Python and tools like IfcOpenShell to enhance IFC model functionality.^35–38

SQL databases provide organized, efficient storage for construction data, facilitating access for multiple users, which is essential for complex projects generating vast information from IFC models.^39–41 This integration with SQL-type databases allows connections with other sources, like cost or maintenance management, enabling reliable data access.^42–45 During data validation, extracted data can be visualized on dashboards to monitor project status and construction activities.^46,47

Research methodology

The research carried out in this article studies the development of a systematization that allows the management of data in building assets from the connection of different software and databases, seeking greater control in the processes of generation, exchange and consolidation of the information during all phases of the asset. To reach that goal, a mixed methodology is used combining deductive and inductive methods. The deductive approach focuses on testing specific hypothesis about the accuracy and efficiency of the new systematization through the development of use cases. These cases will compare the performance of the new systematization with existing methods in terms of accuracy, time of use and ease of use, using metrics obtained directly from the environments and software used.

On the other hand, the inductive approach will be based on direct observation of the new systematization applied to different BIM building models. From this study, qualitative data will be collected through the obtained results and the analysis of recurring patterns. This data will allow to identify areas for improvement and develop new theories on optimizing data exchange. Combining these methods analyzes the effectiveness of the new proposal and provides a solid basis for its implementation. For that purpose, a research method has been developed that structures the study in three differentiated stages (Figure 2): the initial approach; the method development; and the method verification.

Figure 2.

Proposed framework for research method.

This research method begins by identifying connectivity and control issues in defining, integrating, and federating building data within BIM environments, often due to unclear data workflows and lack of centralized databases.³⁹ For this reason, an initial standardization of alphanumeric information is proposed, examining construction data phases, BIM applications, object classification, required information levels, and data types (Figure 1), covering location, units, materials, relationships, etc.

In the second stage, a solution framework and analysis strategy are defined to validate results effectively, ensuring project objectives are met. Automations, enabled by Python and libraries like IfcOpenShell, facilitate coordination of standards and data sources, allowing custom scripts for specific validation tasks. Potential limitations with database-BIM automations are assessed, and extensive testing is performed to identify any performance issues (Figure 2).

The final stage establishes four applicability levels for the systematization proposal (Figure 3). Each level undergoes further study to analyze results and detect inconsistencies, failures, or workflow disconnections.

Figure 3.

Applicability of the systematization proposal in OpenBIM models.

The four-level classification enables precise evaluation of systematization in varying complexity contexts. At the single object level, individual BIM model elements are analyzed to identify detailed errors. The single discipline level assesses the integration of elements within the same specialty. At the multidisciplinary level, the focus expands to coordination and compatibility across disciplines. Finally, the multi-project level addresses data management and integration on a large scale, providing a holistic view across multiple projects. This approach facilitates problem detection at each level and offers a comprehensive perspective on BIM management challenges.

For this research, specific elements and property sets from various BIM uses, disciplines, and phases were selected to create a representative sample (Figure 3). Additionally, OpenBIM models with different IFC scheme versions were examined to study database connectivity.

Moreover, based on current BIM roles and the needs from systematization, four key profiles are proposed (Table 1), each with specific permissions and responsibilities. Although some roles exist in current BIM structures, this proposal introduces new responsibilities for each role.

Table 1.

Roles definition for the proposed systematization.

Role	Permissions	Key responsibilities
Data management administrator	Full database access; manage data and user permissions.	Ensure data quality, implement policies, conduct audits, manage backups, and consolidate BIM data.
BIM data manager	Full access to BIM systems; manage data integration and user access.	Oversee BIM data integrity, integrate data across disciplines, maintain quality standards, and enhance collaboration.
BIM coordinator	Access to all BIM models; use automation for quality control.	Coordinate integration of BIM models, verify quality, and resolve conflicts among teams.
BIM discipline leader	Access to discipline-specific BIM models; modify and review models.	Supervise model creation and updates, coordinate with other leaders, and resolve discipline-specific issues.

To validate the research systematization processes based on other structured studies,^48,49 several evaluation aspects are proposed to assess the applicability of the solution:

(1) Level of Complexity: Challenges involved in the process, including technical capabilities and potential obstacles.

(2) Level of Automation: Degree to which processes are performed automatically with minimal human intervention.

(3) Execution Time: Time taken from start to finish, essential for evaluating efficiency and user experience.

(4) Ease of Use: Simplicity of learning and managing the solution without specialized assistance.

(5) Interoperability: Ability to work across different data systems, regardless of software or platforms used.

(6) Scalability: Capability to expand resources and systems to adapt to new organizational needs.

Therefore, a new method is proposed to standardize building data tasks using a systematic approach based on a set of principles, rules, and techniques that guide the workflow during different phases (Figure 2). This flexible, agnostic approach adapts to various conditions while ensuring consistent processes and data configurations, establishing a framework for optimizing building asset information management.

Systematization development

Once the scope and strategy of the systematization have been established, the practical application is conducted, in which different BIM models are used (Figure 3), along with the creation of a SQL database, different scripts for data management , a set of predefined sheets for information management and other supporting files with the objective of verifying the feasibility and reliability of the process.

Creation of the company’s general database

The general database configuration scheme (Figure 4) highlights two key processes for efficient data entry. One process involves data collection and structuring, analyzing types, sets, and relationships from classification systems and standards. The other focuses on database creation and configuration, defining the overall data system, establishing user roles and permissions, and creating necessary schemas. Automated scripts integrate the structured data into the database, ensuring that all definitions and properties are accurately imported. Initially, only organizational data is collected, with project-specific data added gradually to centralize all technical team models for management, queries, and information exchange.

Figure 4.

Proposed workflow for database creation.

For this section, MariaDB was chosen as the relational database due to its performance, stability, compatibility, and open-source nature, which allows full compatibility with other databases like MySQL. The phpMyAdmin tool from the XAMPP development platform was used for general data administration, offering a highly intuitive interface that simplifies complex configuration and data tasks. Additionally, Apache was established as the server to handle web browser requests.

In reference to information management, the study begins with pre-treatment of the organization’s raw data to normalize datasets and propose new relationships between tables (Figure 5). Associations are established among uses, phases, requirements, classification systems, disciplines, classes, types, property sets, and properties, followed by lower-level relationships like units and data types. The study aims to define the organization’s terms at all levels and link them to third-party definitions from systems like GuBIMClass, Omniclass, and BIMForum, as well as IFC versions 2 × 3, 4, and 4.3.This customization adapts table structures to organizational needs, allowing for easy and rapid scalability.

Figure 5.

Practical application of database configuration.

For database configuration, an initial schema is manually created to store information, followed by user and permission settings (Table 1) to manage data access. Fields in the MariaDB schema include names, descriptions, allowed data types, character length, null values, and key identifications. Records failing these criteria will be rejected, enhancing information quality compared to traditional non-centralized methods. Once the data has been structured and organized, it is transferred to the SQL database through automation routines. Anaconda and Jupyter Notebook were used to develop Python code for data import, utilizing packages like pymysql, openpyxl, and pandas for efficient data transfer from Excel to MariaDB.

Data integration in BIM models

This section covers how to collect information from the MariaDB database for querying and integrating data into BIM models at the start of new projects. Customized SQL queries filter specific information, such as material characteristics and data sets for BIM uses, as well as definition equivalences across languages, IFC versions, or classification systems. To streamline these tasks, scripts and pre-configurations are stored in SQL to automate repetitive queries, optimizing workflows and reducing manual data entry to minimize errors. The proposed process (Figure 6) involves filtering and extracting data, differentiating models by discipline, and grouping information by required uses and phases.

Figure 6.

Data integration in BIM Models.

The resulting tabular data sets are then associated with information models using visual and textual programming scripts. Three processes are defined based on the need to create new properties in BIM models, select values from enumerated lists, or configure IFC export mappings to generate OpenBIM models from native ones.

For data integration in this section of the systematization, Autodesk Revit 2024.2 and Dynamo 2.19 were used alongside Jupyter Notebook and MariaDB. The process is divided into two parts: first, data is extracted from the database, and then it is associated with BIM models.

During extraction, data is selected based on project requirements, including BIM object classes, uses, and application phases, differentiating information sets for each discipline model. The result connects data from various schema tables into a single file for each discipline model. Unlike the schema data, extracted information is unified and grouped for joint application within the exported Excel table. This includes grouping entity classes defined for each property and the allowed IFC classes and types for specific BIM object categories.

In the specific case of the Autodesk Revit application (Figure 7), each property in the table exported from the database contains the information necessary to create the shared parameters for each discipline. Using a programming script with Dynamo, the characteristics of each parameter are extracted, identifying whether it is a type or an instance, the Revit parameter group to which it will be incorporated, the applicable categories and the data type. As a result, a new shared parameters file is created and the parameters are automatically generated in the entity categories that have been indicated.

Figure 7.

Processes for integrating data into BIM models.

The exported file from MariaDB includes data on possible values for project parameters. Users select appropriate values through pop-up windows for preselected instances or element types, using tools like Classification Manager for Revit or scripts executed via Dynamo Player to prevent code alterations. Additionally, an IFC property mapping file is generated from Jupyter, configured for each IFC entity based on the project’s selected version.

Using the models selected for this study (Figure 3), significant improvements in data consistency, coherence, and quality are observed. This ensures all models follow the same procedures and formats, maintaining design integrity and data uniformity. Furthermore, the proposed process enhances compliance with requirements, as property values are typically generated from the database, in contrast to traditional methods where most values are entered manually by users.

Quality controls

Referring to the next section on quality controls, different review studies were conducted to check the adequacy and veracity of the data in the IFC models (Figure 8). These studies include analyzing the nomenclature of entities, psets and properties, the existence of alphanumeric information corresponding to each entity class, data types and units, allowed values for properties with enumerations, minimums and maximums for integer or decimal values, consistency of codes and descriptions of classification systems, models with the classes of entities allowed according to disciplines and checking the version and structure of the IFC required for the project.

Figure 8.

Quality control of OpenBIM Models.

As in previous sections, automated review procedures ensure model quality, generating a report that indicates compliance with BIM model data. If the report is unfavorable, it is sent to the model authoring team for updates. These reviews focus solely on alphanumeric information in the IFC file, not on geometric or visual checks. Other controls related to technical requirements are necessary but fall outside the study’s scope, as many cannot be automated or require advanced programming.

For practical application, IFC models are loaded into the Google Collaboratory environment for automated checks (Figure 9). Using the IfcOpenShell package, data from the file is extracted. Compliance with information requirements is checked against previously extracted templates from MariaDB, considering the IFC version and model discipline. An automated review report is generated, listing all project data that fails to meet requirements and highlighting entities with data issues.

Figure 9.

Processes for performing quality controls on IFC models.

Additionally, tests with Information Delivery Specification (IDS) files are performed to check the information from BIM coordination software by the BIM Coordinator. These files are created using IfcOpenShell definitions and then imported into the BIMCollab platform. Once the information is in the project, the cloud is synchronized with BIMCollab Zoom software and quality checks are executed. After obtaining the results, a BIM Collaboration Format (BCF) file is generated from the platform to be sent to the discipline team.

Consolidation of asset information in the company database

The final section of information systematization proposes the incorporation of building asset data into the MariaDB system (Figure 10), with the aim of centralizing the organization’s information within a single relational and structured data environment. In addition, this opens the door to being able to carry out more detailed and complex future analyses of BIM data models, such as energy performance assessments, cost analysis or logistics planning. By collecting data in this way, it is easier to perform comparative analysis and detect trends or patterns, which can be especially useful for strategic decision-making and long-term planning.

Figure 10.

Data consolidation and visualization.

On the other hand, it is necessary to use unique codes (internal ID) that allow the identification of each element and establish relationships between data tables. In the case of this research, the GUID (Globally Unique Identifier) defined for each IFC entity is used. This identifier is independent of the entity or attribute information, since the information may be prone to change over time, while the GUID will always remain unchanged, achieving traceability of the project information. Once the data is filtered and structured within the coordination software from automated configurations, it is exported within the project’s Common Data Environment and through other automation, it is integrated into the project’s data schema in the database, generating tables for each IFC entity.

To develop a practical example of this process, work using Navisworks Manage 2024 was proposed, in addition to the use of Jupyter Notebook for data management between the BIM environment and the organization’s database. Within Navisworks, the search sets of the IFC entities to be exported are generated, as well as the data definitions from the Selection Inspector section, both definitions extracted from the data exported from MariaDB and generated in the software through automations performed with Jupyter Notebook. In both cases, XML-formatted templates are used, which are generated from MariaDB tabular data. From Jupyter and with the use of Python’s pandas, xml and IfcOpenShell packages, the file structures and information related to the search rules and definitions in each case are created. The tables of the multidisciplinary model are generated and then the data import process is automated again in MariaDB through the use of Jupyter Notebook.

When considering the possibility of associating new data within the Navisworks federated model by connecting other sources or databases with Data Tools, this use case was proposed. However, if the building asset data came only from the IFC models, the information can be extracted directly from these models and integrated into the organization’s database directly, without the need to configure the data sets and templates in the federated model.

Taking into account all of the above, this process allows for the generation of detailed and customized reports that can be used for decision-making and project management, since the data is extracted and integrated with visualization tools to create interactive dashboards that present key project information, in this case, based on the connectivity between MariaDB and PowerBI (Figure 11).

Figure 11.

Processes for data consolidation and visualization.

Results and discussions

After performing all use cases for the practical application of the systematization based on the models in Figure 3, various results were collected (Table 2) to assess the improvement and optimization provided by the proposed solution compared to traditional methods in building assets. Regarding the level of complexity involved in incorporating the new systematization into the organization’s own processes, it is detected that the greatest effort is located at the beginning of the work, when all the templates, connections and automations of each stage must be developed. However, once a new project starts, tasks simplify to updates and maintenance reviews, as processes utilize the same resources, requiring only minor modifications to integrate new data sets. Traditional methods often lead to a lack of coordination between disciplines and human errors in data definition.

Table 2.

Comparative analysis of the proposed systematization.

The levels of automation achieved are deemed sufficient, enabling data transfer between environments and systems, particularly in quality controls (Figure 8), where reviews are automated with a one-click solution. There is potential for further improvement through integration of software APIs or automatic synchronization with the MariaDB database.

Once all the use cases were completed, the study found significant time savings across all analyzed processes due to optimized workflows and ease in defining information at each project stage. This includes savings during project updates and modifications, especially in the data integration stage (Figure 6), where rework is minimized and processes are reused. By establishing clear and simple processes for execution, users only need to select the necessary data for each use, phase, and discipline. Additionally, the new quality control process (Figure 9) greatly simplifies the user’s work, as it is fully automated.

While traditional solutions based on OpenBIM models provide high interoperability, significant barriers hinder the full use of information in IFC files.¹⁸ The new systematization proposal allows data to flow between environments and information systems through relational tables, enhancing data analysis at both project and organizational levels. Additionally, the creation and exchange of open standards like BCF and IDS enable greater interoperability and flexibility in selecting BIM tools.

To verify the permeability and scalability of the solution, random checks were conducted in each use case to assess process viability under varying conditions, such as data filtering and new connection sources. Findings confirmed the potential for systematization growth, emphasizing the importance of understanding the new tools and their integration with the organization’s digital environment, while highlighting the ease of information exchange of the SQL database (Figures 5 and 11) with other management systems.

Practical implications

The proposed systematization (Figure 12) significantly improves traditional BIM work procedures (Table 2) by enhancing coordination among teams and project phases, ensuring everyone works with updated information and reducing errors and delays. It enables more efficient monitoring of progress and quality by maintaining digital continuity of data, regardless of the software used, thus preventing information loss during technological transitions and increasing project resilience. Additionally, centralizing information in a structured manner allows the system to scale and integrate with other resource planning, customer relationship management, and supply chain systems related to building assets.

Figure 12.

Systematization proposal for building asset data management.

Limitations and future studies

This research has studied building asset information management procedures from different perspectives, environments and phases. However, it is necessary to point out some limitations of this research:

• This systematization has not been extensively tested in real projects, which impacts its overall validation. While both a theoretical framework and a practical approach have been developed, the application of the system in the complex environments of actual construction projects remains unverified. Without thorough testing, assessing its effectiveness under varying conditions such as different project scales and stakeholder expectations is challenging. Further studies, including pilot projects, are essential to evaluate its usefulness and gather empirical data. Additionally, feedback from practitioners across different disciplines would provide valuable insights for refinement, ensuring the systematization effectively meets the actual needs of users.

• While this research outlines basic roles for managing and coordinating projects (Table 1), a detailed examination of access levels and information manipulation within databases and BIM environments is necessary to define new user types and permissions based on roles and teams.

• The study of construction objects (Figure 3) focused on select relevant classes, leaving out potential new conditions for classes, relationships, or properties.

Future research should focus on the costs of implementing centralized information systems in the construction industry, including initial investments, maintenance, and potential savings from increased efficiency. Additionally, examining stakeholder resistance to change is crucial, as studies could explore the psychological and organizational factors behind this resistance and identify strategies to facilitate acceptance of new technologies. Future studies should also aim for a greater exploration of procedures established from the use of other software and BIM environments, as well as new proposals for connectivity of the systematization with emerging technologies such as Artificial Intelligence or Machine Learning, which would allow accelerating and optimizing the proposed procedures.

Conclusions

This research highlights the importance of data management and control throughout all phases of building assets, with standardization and structuring of construction data being necessary even before the start of any contract with suppliers. This is due to the complexity of levels and layers of data that a construction project can contain, which is aggravated by the diversity of agents, procedures and solutions that are developed simultaneously.

Any change order in one area of the project is likely to trigger modifications in other areas or specialties, which usually results in cost overruns and delays in the work. To minimize the impact of these changes or unforeseen events, it is essential that BIM models and databases are connected in such a way that the data is updated automatically, allowing all work teams to restructure their activities to adjust to the changes that occur during the phases of the asset’s life cycle.

One of the main contributions of the proposed systematization is to generate a new perspective of centralization of the organization’s information, which allows storing, consulting and managing the data of all projects and understanding the relationships between them, generating a holistic vision of the organization’s building assets in an agnostic and interoperable way. This is achieved thanks to the standardization of data, process automation, the analysis of the IFC standard and the establishment of common criteria for any type of environment, file, relationship, object and property.

By maintaining a common information source that is consistently updated, data can be reused across projects. Depending on each project’s needs, data is filtered and integrated into various BIM software through automated templates and procedures. Throughout the construction process, data is continuously updated, validated, and synchronized within SQL databases, granting data analysts access without the need to use BIM environments.

Footnotes

Acknowledgements

The authors are grateful to the referees of the journal for their extremely useful suggestions to improve the quality of the article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Samuel Jesús Triana González

Rafael Vicente Lozano Diez

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