Automated Fire compliance assessment tool
Fire safety in buildings isn’t just about alarms and sprinklers—it’s about designing spaces that prevent tragedies before they happen. Imagine if architects could assess the fire safety of their designs with just a click, ensuring compliance with global standards during the early stages of construction. Sounds futuristic? Not anymore.
In this blog, we explore how computational tools are revolutionizing the way architects and engineers approach fire safety, making buildings safer.
Fire safety compliance is a critical aspect of architectural design, yet the traditional approach to verifying compliance is often time-consuming, error-prone, and dependent on manual processes. With increasing complexity in building designs and diverse regulatory standards across regions, there is a pressing need for a streamlined, automated solution to address these challenges effectively.
My thesis project aims to transform how fire compliance checks are conducted by leveraging Building Information Modeling (BIM) data, specifically Industry Foundation Classes (IFC), to create a structured and automated framework. The project focuses on parsing spatial, passive, and active fire safety elements from IFC files, converting them into graph-based representations for detailed compliance analysis. By redefining these graphs and integrating rule-based algorithms, the system seeks to provide real-time feedback during the design phase, reducing costly revisions and ensuring adherence to fire safety regulations.
Currently in its first phase, the project focuses on spatial elements like walls, doors, floors, and circulation paths. Future steps include expanding the framework to incorporate passive fire safety features like fire doors and walls, as well as active systems like sprinklers and alarms. The ultimate goal is to develop an interactive user interface that overlays compliance results onto a 3D building model, making the process intuitive and accessible for designers and architects.
This research not only addresses gaps in existing methods but also demonstrates the potential for automated tools to revolutionize fire safety practices, making compliance faster, more accurate, and adaptable to diverse regulatory environments. Follow along as I delve deeper into this exciting journey of innovation!
Fire compliance ensures that buildings adhere to fire safety regulations and standards at local, national, or international levels. These regulations aim to minimize fire risks, enable safe evacuation, and establish systems for fire detection and suppression. Compliance includes proper building design, installation of safety equipment, and regular inspections to maintain adherence to safety standards.
In 2022, there were 1.3 million global fire incidents, with 56% occurring in residential spaces, 32% in commercial settings, and 12% in industrial facilities. These statistics underscore the critical need for integrating effective fire safety measures in architectural design, particularly in residential and commercial domains, which are most vulnerable to fire-related risks.
Automating fire compliance offers two key advantages: enhanced fire safety and improved time efficiency. It ensures adherence to fire codes, identifies potential design-phase risks, and reduces human errors in safety assessments. Additionally, automation streamlines compliance checks, minimizes manual analysis of complex designs, and enables faster iterations and feedback within design processes, significantly improving overall efficiency.
The problem of ensuring fire safety compliance in architectural designs arises due to the limitations of traditional manual methods, which are often time-consuming and prone to errors (Meacham, 2010). While Building Information Modeling (BIM) frameworks, such as IFC, provide structured data for spatial and relational analysis, their potential for automating fire safety remains underutilized (Zhang et al., 2020). The complexity increases with prolonged design revisions, particularly in residential and commercial buildings where fire safety is critical (Tan et al., 2019). Standards like BS 99999:2017 highlight the need for systematic approaches to integrate fire safety into design processes (British Standard Institution, 2017).
The research question focuses on leveraging spatial and relational data from IFC files to automate fire safety compliance assessments. The goal is to enhance the integration of fire safety within architectural designs for residential and commercial buildings, while improving efficiency during design revisions.
The project workflow is a systematic approach divided into seven key stages:
- Research & Planning Framework: Establishing the foundational framework for the study through detailed research and strategic planning.
- IFC Parsing & Data Extraction: Extracting spatial and relational data from IFC files for further analysis.
- Graph Representation & Spatial Analysis: Structuring the data into graph-based models for advanced spatial analysis.
- Compliance Checking with Rulebooks: Automating fire compliance assessments by cross-referencing the extracted data with standardized rulebooks.
- Interactive Visualization Interface: Developing user-friendly visualization tools to interpret compliance results.
- Testing & Validation: Ensuring the accuracy and reliability of the automated system through rigorous testing.
- Presentation & Documentation: Compiling results and insights into a comprehensive and well-documented report for stakeholders.
The “State of the Art” explores two tools, Naviate BIMFire and BIMSMACC, highlighting their strengths in automating fire compliance checks but revealing limitations such as a lack of passive fire safety analysis and limited adaptability for diverse regulations. These gaps emphasize the need for more robust and flexible solutions.
The “State of the Art” for graph structuring evaluates libraries like NetworkX, TopoLogic.py, Neo4j, and Atlas by Nomic. Each offers unique strengths, such as 2D/3D spatial analysis or advanced visualization, but varies in scalability, learning curve, and suitability for complex or small-scale projects. These insights guide tool selection for effective graph-based fire compliance analysis.
The “State of the Art” for rule-based systems explores approaches like IFC-based rule checking, integrating knowledge graphs with large language models, and BIM-KBMS systems for MEP compliance. These methods streamline automated compliance checks, enhance construction scheme evaluation, and address complex rule management in design workflows.
The Fire Sense user workflow involves uploading IFC files, which are ingested and converted into graph structures for compliance checks against selected rulebooks. Results are displayed as overlays in the IFC.JS preview, allowing users to view outputs and iterate through a feedback loop if necessary. This streamlined process automates compliance assessment efficiently.
This diagram highlights the critical components needed for fire compliance, including passive protections (e.g., fire doors and walls), the placement of fire extinguishers, emergency exits and lighting, fire sprinkler systems, and fire alarm and detection systems. These elements form the backbone of a detailed fire safety plan.
This graphic illustrates the parsing of a sample IFC file using the IFC OpenShell library, extracting raw data to analyze architectural elements. It raises the question of which specific elements should be parsed for compliance, focusing on efficiency and relevance to fire safety.
Categorizes essential IFC elements required for compliance checks into spatial structures (e.g., walls, floors), building components (e.g., doors, roofs), material properties (e.g., fire resistance), spatial relationships (e.g., voids, containment), geometry, and fire safety systems (e.g., sprinklers, alarms). These classifications guide the parsing process.
Outlines a three-stage parsing methodology for compliance checks. Stage 1 focuses on spatial elements (e.g., walls, floors), Stage 2 incorporates passive fire safety elements (e.g., fire doors, walls), and Stage 3 evaluates active fire safety systems (e.g., sprinklers, alarms), ensuring a comprehensive assessment.
Explains the rationale for using graph-based methods in compliance checks, emphasizing their ability to model spatial and relational data, handle diverse and incomplete designs, and visually represent complex architectural layouts for improved analysis.
Highlights the need to redefine IFC graph structures for fire compliance by aligning them with rule logic, simplifying data processing, and ensuring scalability. This approach improves accuracy, reduces unnecessary data, and supports future extensions.
Demonstrates how various node-edge relationships (e.g., spatial, inter-element) are defined and visualized in graph structures. Examples include building-storey relationships, spatial paths, and connections between safety elements like extinguishers and exits.
Shows how a 3D building model is converted into a graph structure, illustrating connections between architectural and fire safety elements. This provides a clear visual representation for evaluating compliance.
Explains the utility of graph-based methods for spatial analysis, such as calculating egress distances. This ensures compliance by evaluating spatial paths and relationships between elements like exits and rooms.
Visualizes the final compliance results by classifying graph nodes into compliant and non-compliant categories. Shortest path metrics and other criteria are used to determine areas requiring improvements.
This illustration showcases the logic behind spatial graph representation, connecting structural (facades, columns), circulation (ramps, stairs), and spatial elements (walls, doors, windows) into a cohesive hierarchy for compliance analysis.
This table demonstrates how passive fire safety elements like fire walls, doors, materials, and escape routes are represented in the graph. It highlights required attributes, adjacent nodes, and their purpose in ensuring fire safety.
Details active fire safety systems, such as alarms, sprinklers, and extinguishers, emphasizing their adjacency relationships, required attributes, and their critical roles in fire detection, suppression, and evacuation safety.
Depicts an IFC model represented in raw graph form using tools like NetworkX and Pyvis. The graph contains all extracted nodes, showcasing the complexity of translating architectural data into graph format for analysis.
Presents a simplified graph extracted from IFC data, focusing on key elements like building levels and spaces. The visualization, created with Pyvis, provides a more structured and readable representation for compliance evaluation.
This diagram outlines the use of the UK Building Regulations 2022 as a reference for fire compliance. Design, passive, and active elements are categorized under relevant British Standards for proof of concept and rule-based validation.
Highlights a structured approach to organizing compliance data. Categories include active/passive elements, design elements, and materials, each detailing attributes like IDs, compliance parameters, and descriptions for standardized checks.
Provides an example of how compliance rules are formatted, emphasizing the importance of a standardized, scalable format. The JSON example showcases detailed parameters, compliance criteria, and violation messages for validation.
The project is currently in its first phase, focusing on spatial graph representation and rulebook integration for fire compliance. Future steps involve refining the graph structure for passive and active fire safety elements, implementing algorithms for automated compliance checks, and expanding the scope to support multiple regulatory frameworks. In the second term, efforts will center on validating algorithms against real-world datasets and creating modular rulebook structures. The third term will focus on building a user interface for seamless interaction, including visual overlays for 3D compliance feedback. Additional tasks include optimizing the system for incomplete datasets and testing scalability to diverse building types, ensuring the tool’s adaptability and robustness in achieving the goal of fully automated fire compliance.
