Artificial intelligence is beginning to reshape fire safety — from automated fire modelling to AI-assisted compliance checking. Here's what the future looks like.
10 December 20244 min readFire Safety Services
AI and Fire Safety: Where We Are Now
Artificial intelligence is beginning to influence fire safety consultancy in ways that range from incremental efficiency improvements to potentially transformative changes in how fire safety analysis is conducted. The applications that are currently most mature — AI-assisted CFD pre-processing, machine learning for fire detection optimisation, and large language models for regulatory research — are already in use by some fire engineering firms. More ambitious applications, including AI-generated fire strategies and autonomous fire modelling, remain at an earlier stage of development.
Understanding what AI can and cannot currently do in fire safety is important for anyone commissioning fire safety services or working in the sector. AI tools can accelerate certain aspects of fire engineering work, but they cannot replace the professional judgement of a chartered fire engineer — and for higher-risk buildings, that professional judgement is a regulatory requirement, not a preference.
Current AI Applications in Fire Safety
AI-assisted fire detection — machine learning algorithms are being used to improve the sensitivity and specificity of fire detection systems, reducing false alarm rates while maintaining early detection capability. Video-based fire and smoke detection using computer vision is an area of active development.
CFD pre-processing and optimisation — AI tools are being used to assist in the setup of computational fluid dynamics fire models, reducing the time required to prepare model inputs and potentially improving the quality of model configurations.
Regulatory research and document analysis — large language models are being used to assist fire engineers in researching regulatory requirements, identifying relevant guidance, and extracting information from large document sets. This can reduce research time but requires careful verification of AI-generated outputs.
Building inspection and defect identification — computer vision systems are being used to assist in the identification of fire safety defects during building inspections, including fire door defects, unsealed penetrations, and missing cavity barriers.
Predictive fire risk analytics — machine learning models trained on fire incident data are being developed to predict the relative fire risk of different building types, locations, and occupancies, potentially supporting more targeted fire safety management.
AI can assist fire engineers in working more efficiently and can augment certain analytical tasks. It cannot replace the professional judgement, site experience, and regulatory knowledge of a chartered fire engineer — and for higher-risk buildings, the Building Safety Regulator expects human competence at the centre of the fire safety process.
Where AI Cannot Replace Human Judgement
The core of fire safety engineering — the professional judgement that determines whether a building is fire safe, the interpretation of ambiguous regulatory requirements, the assessment of novel design situations, and the communication of complex fire safety risks to non-specialist clients — requires human expertise that current AI systems cannot reliably replicate.
For higher-risk buildings, the Building Safety Regulator expects fire strategies to be produced by and under the responsibility of a Chartered Engineer. AI tools may assist in producing those strategies, but the professional responsibility for their content rests with the human engineer. An AI-generated fire strategy that has not been reviewed and validated by a competent chartered fire engineer is not an acceptable submission for Gateway 2.
The Longer-Term Outlook
Over the next decade, AI is likely to play an increasing role in fire safety — from more sophisticated fire detection systems to more efficient fire modelling tools to AI-assisted building safety case management. The fire engineering profession will need to develop frameworks for the appropriate use of AI tools, including standards for the validation of AI-generated outputs and clarity on where professional responsibility lies when AI tools are used.
The fundamental requirement — that higher-risk buildings are designed and managed by competent professionals with the knowledge, skills, and experience to keep occupants safe — is unlikely to change. What AI may do is allow those professionals to work more efficiently and to provide higher-quality services to more clients.
Frequently Asked Questions
Can AI produce a fire strategy report?
AI tools can assist in producing fire strategy reports — for example, by generating initial drafts based on building information, or by checking drafts against regulatory requirements. However, an AI-generated fire strategy must be reviewed and validated by a competent chartered fire engineer before it can be relied upon. For higher-risk buildings, the BSR expects human professional responsibility at the centre of the process.
How is AI being used in fire detection?
Machine learning is being used to improve detector sensitivity and specificity, reducing false alarms while maintaining early detection. Video-based fire and smoke detection using computer vision is an active area of development, particularly for large open spaces and high-risk environments where conventional detection is challenging.
Will AI reduce the cost of fire engineering services?
AI tools that reduce the time required for research, document preparation, and routine analysis may reduce costs for some aspects of fire engineering services. However, the core of fire engineering — professional judgement and regulatory engagement — is unlikely to be significantly reduced in cost by current AI tools.
Is AI being used in building control or BSR assessments?
Not significantly at present. Building control assessments and BSR gateway reviews remain human-led processes. AI tools may in future be used to assist assessors in reviewing large volumes of documentation, but regulatory decisions require human accountability.
What are the risks of using AI in fire safety?
The primary risks are over-reliance on AI-generated outputs without adequate human validation, and the use of AI tools that have been trained on data that does not reflect current UK regulatory requirements. Fire safety outputs must be validated by a competent professional before they are relied upon.
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