Reducing False and Improving Response Time in High-Occupancy Buildings: A Quantitative Study of NFPA 72-Complaint Cause-and-Effect Testing Outcomes

Abstract

This study examined the effectiveness of NFPA 72-compliant cause-and-effect testing in reducing false alarm frequency and improving response time in high-occupancy buildings through a quantitative quasi-experimental design. Data were collected from 28 buildings, including hospitals, educational institutions, commercial complexes, and high-rise office structures, comprising a total of 12,460 recorded alarm events. Pre-testing and post-testing system performance metrics were analyzed to evaluate the impact of structured testing interventions. The results indicated a substantial reduction in false alarm frequency, with mean values decreasing from 14.8 to 7.2 events per building per month, representing an overall reduction of 51.4%. Response time analysis revealed a significant improvement, with mean latency decreasing from 9.2 seconds to 5.6 seconds, reflecting a 39.1% reduction and improved consistency across systems. Statistical analysis confirmed that these changes were significant, with large effect sizes observed for false alarm reduction and moderate-to-large effect sizes for response time improvement. Sub-group analysis demonstrated that commercial complexes achieved the highest reduction in false alarms at 56.3%, while high-rise office buildings showed the most consistent response time improvements. Multi-sensor detection systems outperformed single-sensor technologies, achieving a false alarm reduction of 58.6%, compared to 47.2% and 42.5% for photoelectric and ionization detectors, respectively. Additionally, fully integrated systems exhibited greater performance gains, with response time improvements averaging 44.3%, compared to 31.8% in partially integrated systems. The findings demonstrated that cause-and-effect testing significantly enhanced system reliability by ensuring accurate input-output relationships and reducing configuration errors. The integration of statistical analysis and performance benchmarking provided robust evidence of the effectiveness of structured testing in optimizing fire alarm system performance. The study contributed to the advancement of quantitative fire safety evaluation by highlighting the importance of systematic verification and data-driven analysis in high-occupancy building environments.