Development of a Performance-Based Approach for Optimizing Sensor Placement in Building Fire Detection

Fire detection systems play a vital role in ensuring effective fire protection within buildings. At present, the placement of fire detectors is guided by established codes and standards, which specify maximum coverage areas for each detector. Building engineers typically follow these guidelines, positioning detectors strategically to achieve full coverage. While this approach provides adequate protection, it fails to consider the impact of varying environmental factors in different settings and accurately assess the actual performance of fire detection systems. This limitation is particularly evident in unique spaces like warehouses, where fire types and potential ignition locations may differ significantly from those in conventional environments, necessitating a more customized approach to sensor placement. To address this issue, a fire detection performance-based sensor placement optimization (FDPB-SPO) approach is proposed. This methodology integrates numerical datasets generated from multiple simulated fire scenarios with advanced optimization algorithms to evaluate fire sensor placement performance and identify the optimal arrangement. The optimization process balances effective fire detection with compliance to code requirements, ensuring both enhanced performance and practical applicability. A case study evaluating this proposed approach demonstrates its effectiveness in determining the more appropriate arrangement for fire detection. Additionally, integrating it with the Genetic Algorithm (GA) yields an optimized solution that enhances fire detection performance and reliability. These findings highlight the potential of the FDPB-SPO approach in advancing sensor placement strategies and contributing to the development of future fire detection system standards.