IFC File Analyzer Software.

Extracting architectural elements from Industry Foundation Classes (IFC) files plays an important role on indoor air quality assessment. However, the traditional methods may extract useless instances and miss some necessary information, which results in poor air quality assessment. To address the above issues, this paper proposes an attribute extraction method for air quality assessment from IFC files, called as IFC-AAE. First the instances of the IFC file are preprocessed to remove the redundancies. Next the entity instances related to air quality assessment are extracted and then classified based on floors. Finally, the attribute information of these entities is extracted according to their reference relationship. The experimental results show that the IFC-AAE method is superior than the previous methods. Compared with the IFC file analyzer, the IFC-AEE method generates fewer invalid data. Compared with the Map-based extract method, the IFC-AEE method has an improvement by 4.78% on the precision rate on average.

The accurate and efficient calculation of floor areas is a critical aspect of building design processes, yet it remains a challenging task due to the intricate rules and regulations that must be adhered to. Traditional methods, which rely on manual calculations, are not only time-consuming but also prone to human error, highlighting the need for an automated solution. Building Information Modeling (BIM) can support the automated solution by providing digital representations of building designs. The openBIM workflow enables collaborative work among various stakeholders, allowing them to share and integrate project information seamlessly without being locked into proprietary systems. While Industry Foundation Classes (IFC) files serve as a robust digital representation of BIM, facilitating data exchange across different software platforms, the complexity associated with processing these files poses a significant barrier to ordinary users. To address these gaps, we propose an innovative semi-automated floor area calculation system that leverages state-of-the-art Large Language Model (LLM) agent. This system is designed to efficiently and accurately extract relevant information from IFC files applying specific filtering conditions and then perform the necessary calculations to determine floor areas according to the required standards. Due to LLMs’ characteristic of interacting using natural language, our approach significantly reduces the technical barriers faced by non-expert users, making the process of floor area calculation more accessible and user-friendly. We validate the proposed method through the practice of simplified floor area calculation of an academic building, offering a promising solution to streamline BIM workflows and enhance productivity in the architecture, engineering, and construction (AEC) industries.

Building Information Modelling (BIM) could provide detailed 3D building models to Geographic Information Systems (GISs) for the emerging studies in smart cities/digital cities. During information exchange, Industry Foundation Classes (IFC) containing rich building information is the most used data format in BIM. However, GIS does not the capacity to directly consume the data in IFC due to data incompatibility between the two systems. Consequently, IFC files have to be transformed into a format that is accessible to GIS. Shapefile is one of them, given its popularity in geospatial science. This study tests the different formats and methods for transforming IFC to shapefile, assesses their performance, in order to offer a ranking, from which better decisions could be made when choosing an appropriate intermediate data format. The results show direct transformation from IFC to shapefile retained the most geometry and semantic information. If the direct transformation method does not work in some cases, 3DS, VRML, or SKP could be used as alternatives to retain most of the information, while DWG, DXF, KML, and KMZ should not be considered due to their poor performance in the test.

Indoor navigation supported by the Industry Foundation Classes (IFC): A survey

The assessment of Industrialized Building System (IBS) adoption in construction projects—a critical metric for evaluating prefabrication levels and construction modernization—remains largely manual, time-intensive, and prone to inconsistencies, with practitioners typically requiring 4–8 h to evaluate a single building using spreadsheet-based frameworks and visual documentation review. This paper presents a novel AI-enhanced workflow architecture that automates IBS scoring through systematic processing of Industry Foundation Classes (IFC) building information models—the first documented integration of web-based IFC processing, visual workflow automation (n8n), and large language model (LLM) reasoning specifically for construction industrialization assessment. The proposed system integrates a web-based frontend for IFC file upload and configuration, an n8n workflow automation backend orchestrating data transformation pipelines, and an Azure OpenAI-powered scoring engine (GPT-4o-mini and GPT-5-0-mini) that applies Construction Industry Standard (CIS) 18:2023 rules to extracted building data. Experimental validation across 136 diverse IFC building models (ranging from 0.01 MB to 136.26 MB) achieved a 100% processing success rate with a median processing duration of 61.62 s per model, representing approximately 99% time reduction compared to conventional manual assessment requiring 4–8 h of expert practitioner effort. The system demonstrated consistent scoring performance with IBS scores ranging from 31.24 to 100.00 points (mean 37.14, SD 8.84), while GPT-5-0-mini exhibited 71% faster inference (mean 23.4 s) compared to GPT-4o-mini (mean 80.2 s) with no significant scoring divergence, validating prompt engineering robustness across model generations. Processing efficiency scales approximately linearly with file size (0.67 s per megabyte), enabling real-time design feedback and portfolio-scale batch processing previously infeasible with manual methods. Unlike prior rule-based compliance checking systems requiring extensive manual programming, this approach leverages LLM semantic reasoning to interpret ambiguous construction classifications while maintaining deterministic scoring through structured prompt engineering. The system addresses key interoperability challenges in IFC data heterogeneity while maintaining traceability and compliance with established scoring methodologies. This research establishes a replicable architectural pattern for BIM-AI integration in construction analytics and positions LLM-enhanced IFC processing as a practical, accessible approach for industrialization evaluation that democratizes advanced assessment capabilities through open-source workflow automation technologies.

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

IFC (Industry Foundation Classes) has been widely adopted in BIM (Building Information Modeling) for information interchange. Actual data exchange relies heavily on the IFC support of target application. This paper introduces a generic way of accessing IFC model business data as needed. In this paper, we will present 1) a concept and a method to define tabular views of IFC data for downstream uses, 2) an algorithm to transform IFC data to defined tabular views, 3) a tool of exporting transformed results to ODBC compliant applications e.g. MS Office etc. in the context of i-model, a selfdescribing, read-only graphic container containing information with engineering precision, and 4) a case study to create COBie handover from IFC using this tool.

With the development of computer processors, vast numerical simulation tools are widely used by fire engineers to determine the spread of fire and smoke. However, the fire modeling practices are often highly time‐consuming and cost‐intensive especially for building geometry information which does limit the further implementation of building fire safety analysis. Although nowadays building information modeling (BIM) has become a buzzword in the Architecture, Engineering, and Construction (AEC) field to facilitate information integration and interoperability, data sharing and exchange are still weak in the traditional interoperability between BIM applications and fire simulation software, since the data schema of them are totally different. In this paper, a quick and accurate approach for information sharing for building fire safety analysis between mainstream BIM applications and widely used fire simulation software has been successfully implemented, based on Industry Foundation Classes (IFC) and Fire Dynamics Simulator (FDS). And, both geometrical building information and sematic information can be shared by this approach through the transformation of coordinate systems, outer database, and IFC file extension. The BIM model restoring fire simulation results can support other performance‐based design of building, such as structural fire‐resistant design and evacuation design. And, all of the analysis results including building fire simulation, structure safety design, and evacuation simulation can be integrated in Autodesk Revit, establishing a framework of IFC‐ and FDS‐based information sharing for building fire safety analysis successfully. A gymnasium has been taken as a case study to illustrate the capability of this framework.

Implementation of BIM + WebGIS Based on Extended IFC and Batched 3D Tiles Data: An Application in RCC Gravity Dam for Republication of Design Change Model

The Industry Foundation Classes (IFC) data model enables vendor-neutral data exchange across Building Information Modeling (BIM) processes. However, IFC has limitations in transferring underlying design logic, including geometric constraints between components and reference grids. The absence of grids hinders efficient IFC model reuse, especially when design modifications depend on them. To address these challenges, this paper introduces an automated method to enrich models with reference grids. Component positions are calculated from semantic and geometric model information, with a Genetic Algorithm (GA) applied to reduce unbound components and irregular grid spacing using numerical thresholds. The generated IfcGrid elements and dependencies are integrated into the IFC model to facilitate design modifications. The approach is demonstrated on eight models from different BIM tools, with its usability and efficiency validated through an expert survey. While refinement is needed for complex scenarios, the approach advances IFC enrichment and supports more efficient BIM workflows. • Grid enrichment by re-engineering spatial placement logic in design models. • Multi-threshold location alignment mechanism using IFC geometry and semantic attributes. • RRGA-based method providing designers with reference grids. • Streamlined workflow for converting IFC models into editable BIM models.

Recently, interoperability between BIM (Building Information Model) and BEM (Building Energy Model) has been highlighted. Even though it has been widely recognized that there are significant potentials in BIM by data sharing and reuse, BIM has not been actively utilized for building energy simulation. With this in mind, in this paper, the authors investigated several issues: (1) limitation of Industry Foundation Class (IFC) and BIM authoring tools, (2) immature IFC data schema, (3) data mapping problem between BIM and BEM. Firstly, IFC files exported from major BIM authoring tools are not consistent with each other and don't include significant thermal parameters. In addition, building geometry, e. g. underground wall, is not properly exported. IFC data schema are not ready to describe detailed information on thermal and dynamic properties (efficiency, performance curves, etc.) of mechanical systems (HVAC, AHU), delivery systems (fans, pumps), and plants (boilers, chillers), which are very essential for BEM. Finally, most of the efforts on BIM-to-BEM are focused on data conversion (data-driven approach), without considering dynamic architectural design process which involves many different stakeholders. In near future, BIM-to-BEM interface should take into account design context and rational decision making so called 'process-driven approach'.

Planning of the built environment requires two-levels of planning process, city/neighborhood-scale and building-scale levels. At the city/neighborhood-scale, Geographic Information System (GIS) is commonly used with CityGML as its open-source 3D format. Meanwhile, for building-scale, Building Information Modelling (BIM) is used, and Industry Foundation Classes (IFC) format is its open-source file format. Both technologies work on different data formats and data exchanges. The research is focusing on ways of exchanging information and bringing together CityGML and IFC. With local context input, the methodology could be considered as a framework to parametrically manage the information related to energy, environment, security, etc. In this project, two use cases were developed, such as visualization for a web application. Autodesk Revit and Graphisoft Archicad were used in developing the building models as a prototype for the transformation testing. The transformation system was developed using Feature Manipulation Engine (FME), by Safe Software. FME allowed us to restructure the data model (IFC) and transformed it to the destination data format (CityGML). The test results showed that from detailed BIM models, CityGML format, as well as a Sketchup file, could be generated. These models can be imported to web visualization applications for urban energy modelling.

Building Information Modeling (BIM) and Industry Foundation Classes (IFC), which is another key concept of BIM, are becoming globally-recognized standards. Although BIM education in schools is important in order to advance the adoption of BIM in the industry, such education has yet to be achieved. This study examines the degree of awareness and acceptance of BIM and BIM education among architecture students in Asia. A survey was conducted to measure recognition, interest, and experience with BIM and IFC among students participating in a design workshop in Busan, Korea. The students were moderately aware of BIM itself, yet had no familiarity with IFC. The more they know about BIM, the more likely they are to have an interest in it. Korean students are more aware of BIM than students from other Asian countries, and ′Upper class′ (3rd year and higher) students are more aware than ′Lower class′ (1st and 2nd year) students. This difference in awareness may be due to the strong government led initiative in Korea. Although for effective adoption of BIM and IFC, they should be introduced earlier to raise an awareness and interest in them.

The upcoming next generation of the Internet, often referred to as the semantic Web, will bring a lot of new technologies, one of which is the Web ontology language (OWL). One feature of this language is to support interoperability between software applications, including Web services and intelligent agents. Another technology to bring interoperability between software programs is ISO-10303 Standard Exchange of Product data (STEP). The industry foundation classes (IFC) have been developed as a building information model for the building and construction industry using technology from STEP. Converting IFC to OWL can be done on different ways. A unidirectional conversion from an IFC to an OWL has been developed for research purposes. The developed prototype converts not all the IFC data to OWL but supports the search for a more appropriate mapping. As the conversion is not straightforward, reengineering the building model to gain more advantages of functionality offered by OWL has to be investigated.

The World Wide Web has provided a vast distributed information space that is primarily designed for human consumption. The enmeshment of the Web and various business processes has made it imperative to define business information in a format that facilitates machine-to-machine exchange and automated processing. The construction industry is not an exception; construction business processes depend on a diverse set of information sources. For efficient processing of the construction business information, it is necessary to publish the information in a format that allows computer applications to easily discover, query and share the information. Currently, the standard for representing, accessing, and sharing a building model is the Industry Foundation Classes (IFCs) data model. Several researchers have investigated the limitations of IFCs data model for interoperability among heterogeneous software applications. This paper presents the results of an on-going research on an ontology-based approach to building information modeling to facilitate information exchange among different knowledge domain applications. The approach developed in this study is based on a shared building ontology that models building domain element types and element relationships. The information systems to be integrated should be modeled using the shared ontology; each knowledge domain adds its own element properties to the shared building ontology. SWRL rules are used for mapping element properties from one domain to another. An example demonstrates how the developed approach can facilitate project information sharing between design and estimating domains.