Identification of Multiple Fire Sources in the Utility Tunnel Based on a Constrained Particle Swarm Optimization Algorithm

Liu, Xiaojiang; Sun, Bin; Xu, Zhao‐Dong; Liu, Xuanya; Xu, Dajun

doi:10.1007/s10694-022-01284-5

Cited by 10 publications

(1 citation statement)

References 40 publications

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“…Since both traditional optimization algorithms and sequence methods can only get suboptimal solutions, intelligent optimization algorithms are more widely used for such a problem. Commonly used intelligent optimization algorithms are usually swarm-based intelligence, including the particle swarm optimization (PSO) algorithm [11][12][13][14] and the ant colony optimization (ACO) algorithm [15]. These algorithms have been used to determine the tunnel fire sources and to optimize the sensor layout.…”

Section: Introductionmentioning

confidence: 99%

Risk and Energy Based Optimization for Fire Monitoring System in Utility Tunnel Using Cellular Automata

Zhang,

Bai,

Diao

et al. 2024

Sustainability

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Fire is one of the biggest threats to the safety of utility tunnels, and establishing camera-based monitoring systems is conducive to early fire finding and better understanding of the evolution of tunnel fires. However, conventional monitoring systems are being faced with the challenge of high energy consumption. In this paper, the camera operation in a utility tunnel was optimized considering both fire risk and energy consumption. Three design variables were investigated, namely the camera sight, the number of cameras in simultaneous operation, and the duration of camera operation. Cellular automata were used as a simple but effective method to simulate the spread of fire in a utility tunnel. Results show that as the number of cameras in simultaneous operation increases, the probability of fire capture also increases, but the energy consumption decreases. A shorter duration of camera operation can lead to a higher probability of fire capture, and meanwhile, lower energy consumption. For the duration of camera operation shorter than or equal to the allowable time, the probability of fire capture is significantly higher than that for the duration longer than the allowable time. Increasing the camera sight will significantly increase the probability of fire capture and lower the total energy consumption when a blind monitoring area exists. The total energy consumption of a camera-based monitoring system roughly satisfies hyperbolic correlation with the duration of camera operation, while the probability of fire capture can be predicted based on the number of cameras in simultaneous operation through a power model. The optimal design for the modeled tunnel section is two cameras in simultaneous operation with a tangent monitoring area. The duration of camera operation should be as short as possible, at least shorter than the allowable time. The study is expected to provide a reference for the sustainable design of energy-saving utility tunnels with lower fire risk.

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