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Aggregated results of our survey on the use of AI in fire safety engineering

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123 respondents

1. Role Profile

Response Count %
Associate / Director 41 33.3%
Junior / Fire Engineer 31 25.2%
Senior / Principal Engineer 31 25.2%
Consultant / Fire Risk Assessor 10 8.1%
Academic / Researcher 5 4.1%
AHJ / Regulator 2 1.6%
Student 2 1.6%
Principal Fire Engineer / Training & Development Manager 1 0.8%

 

2. Years of Experience

Response Count %
15+ years 39 31.7%
0–5 years 37 30.1%
5–10 years 27 22.0%
10–15 years 20 16.3%

 

3. AI Familiarity

Response Count %
Occasional use 56 45.5%
Regular use 41 33.3%
Aware but not using 15 12.2%
Advanced / expert use 9 7.3%
Not familiar 2 1.6%

 

4. Professional AI Use

Response Count %
Yes, occasionally 55 44.7%
Yes, frequently 31 25.2%
No, but interested 25 20.3%
No, and do not intend to 12 9.8%

 

5. Perceived Effectiveness

Response Count %
Somewhat effective 63 51.2%
Effective 39 31.7%
Not effective 12 9.8%
Very effective 9 7.3%

 

6. Estimated Weekly Time Savings

Response Count %
1–3 hours 39 31.7%
< 1 hour 34 27.6%
No response 24 19.5%
5+ hours 13 10.6%
3–5 hours 13 10.6%

 

7. Independent Checking

Response Count %
Yes, without exception 96 78.0%
Yes, for technical work only 25 20.3%
Sometimes 2 1.6%

 

8. Professional Risk

Response Count %
Slightly increases risk 46 37.4%
Significantly increases risk 35 28.5%
No material impact 29 23.6%
Slightly reduces risk 11 8.9%
Significantly reduces risk 2 1.6%

 

9. Impact Over Next 5–10 Years

Response Count %
Mixed impact 72 58.5%
Mostly beneficial 35 28.5%
Mostly negative 9 7.3%
Unsure 7 5.7%

 

10. Regulator Expectations

Response Count %
Yes, slightly 44 35.8%
No 33 26.8%
Yes, significantly 24 19.5%
Unsure 22 17.9%

 

11. Organisational Policy

Response Count %
Yes, formal policy 63 51.2%
Under development 27 22.0%
No guidance 18 14.6%
Informal guidance 15 12.2%

 

12. Awareness of Professional Body Guidance

Response Count %
No 83 67.5%
Yes 21 17.1%
Unsure 19 15.4%

 

13. Sector (multi-select)

Response Count % of respondents
Consultancy / Design 109 88.6%
Research / Academia 16 13.0%
Expert Witness 6 4.9%
Insurance / Risk 4 3.3%
Contracting 4 3.3%
Client / Developer 3 2.4%
Regulatory / Approval body 3 2.4%
Other 7 5.7%

 

14. AI Tools Used (multi-select)

Response Count % of respondents
Large language models (e.g. ChatGPT, Copilot) 108 87.8%
Research or literature review tools 40 32.5%
Data analysis / automation tools 27 22.0%
Image or drawing interpretation tools 23 18.7%
Coding / scripting assistance 23 18.7%

 

15. Current & Potential Use Areas (multi-select)

Response Count % of respondents
Administrative tasks (emails, proposals, summaries) 95 77.2%
Drafting reports and technical narratives 69 56.1%
Interpreting guidance and standards 59 48.0%
Data analysis (e.g. evacuation modelling outputs) 49 39.8%
Quality assurance / peer review 38 30.9%
Fire strategies and design optioneering 35 28.5%
Performance-based design support 29 23.6%
Training and mentoring junior engineers 18 14.6%

 

16. Main Concerns (multi-select)

Response Count % of respondents
Accuracy and incorrect output 115 93.5%
Misinterpretation of regulations or standards 101 82.1%
Over-reliance by junior engineers 96 78.0%
Professional liability and accountability 76 61.8%
Data confidentiality / intellectual property risk 75 61.0%
Ethical considerations 69 56.1%
Lack of transparency in outputs 56 45.5%
No significant concerns 2 1.6%

 

17. Roles & Skills Growing in Importance (multi-select)

Response Count % of respondents
Engineering judgement or interpreting guidance 95 77.2%
Ethical and professional oversight 80 65.0%
Performance-based design expertise 80 65.0%
Fire science fundamentals 66 53.7%
Communication with regulators 59 48.0%
Programming / data literacy 37 30.1%

 

18. Preferred Form of Guidance (multi-select)

Response Count % of respondents
Ethical framework 102 82.9%
Practical dos and don’ts 92 74.8%
Training courses 64 52.0%
Case studies 60 48.8%
CPD guidance 57 46.3%
Model disclaimers / statements 54 43.9%

 


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