Intelligent fire location detection approach for extrawide immersed tunnels

Zhang, Zhen; Wang, Liang; Liu, Songlin; Yin, Yunfei

doi:10.1016/j.eswa.2023.122251

Cited by 6 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kong et al [ 22 ] got a positive correlation between the electromagnetic radiation signals and coal temperature and presented the conceptual design of a method to detect concealed fire. Zhang et al [ 23 ] integrated soot concentration and temperature data to compile a comprehensive dataset for the task of fire location detection in ultra-wide immersed tube tunnels.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional inverse problem of fire location in the closed goaf of coal mine based on optical fiber sensors

Liu,

Chen,

Wang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Monitoring the temperature to determine the fire source locations is essential for controlling the spontaneous combustion in the goaf. Optical fiber sensors are employed to measure the temperature distribution in the goaf. However, due to changes in the geological conditions and the influence of the falling rocks in the goaf, only sensors on the upper side of the uncompacted goaf, due to inclination and coal pillar, may remain. Unilateral sensors are located on the upper side of the goaf, while fire occurs in the center. To investigate the issue with linear unilateral sensors, a two-dimensional inverse method has been developed to determine the location of fire sources by considering heat transfer after a fire inside the goaf. The equations were theoretically solved using Green’s function method to obtain the internal temperature distribution of the physical model of the goaf. Sensitivity analysis identified the most crucial parameters in the process of spontaneous heating at different temperature. The fire source location can be determined using a loop method based on the model calculations. We considered a case to validate the model. Accurately identifying the fire source location in the goaf using the unilateral sensors has an essential theoretical and practical significance for fire prevention and fighting.

show abstract

Section: Introductionmentioning

confidence: 99%

Two-dimensional inverse problem of fire location in the closed goaf of coal mine based on optical fiber sensors

Liu,

Chen,

Wang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In recent years, the advances in artificial intelligence (AI), machine learning [17], and AI modeling [18] have witnessed a rapid growth in the application of intelligent monitoring like fire source identification and optimal layout design [19,20]. These techniques may include artificial neural network (ANN) [21], convolutional neural network (CNN) [8,22], transpose convolution neural network (TCNN) [23], BP neural network [24,25], long short-term memory network (LSTM) [21,23,26] or LSTM recurrent neural network [27], bidirectional long short-term memory (BiLSTM) [28], random forests (RF) [26], as well as other machine learning algorithms like Bayesian network (BN), support vector regression (SVR), and multilayer perceptron (MLP) [26,29]. Different machine learning algorithms can also be employed in combinations like CNN-LSTM or CNN-BiLSTM [28].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques may include artificial neural network (ANN) [21], convolutional neural network (CNN) [8,22], transpose convolution neural network (TCNN) [23], BP neural network [24,25], long short-term memory network (LSTM) [21,23,26] or LSTM recurrent neural network [27], bidirectional long short-term memory (BiLSTM) [28], random forests (RF) [26], as well as other machine learning algorithms like Bayesian network (BN), support vector regression (SVR), and multilayer perceptron (MLP) [26,29]. Different machine learning algorithms can also be employed in combinations like CNN-LSTM or CNN-BiLSTM [28]. In addition to the two mainstream methods of intelligent optimization and machine learning, the layouts of sensors can also be optimized through numerical simulation [30,31], and the performance of dynamic sensor networks has also been investigated [32,33].…”

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

View full text Add to dashboard Cite

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.

show abstract

A novel unmanned aerial vehicle driven real-time situation awareness for fire accidents in chemical tank farms

Sheng,

Chen,

et al. 2024

Journal of Loss Prevention in the Process Industries

View full text Add to dashboard Cite

Intelligent fire location detection approach for extrawide immersed tunnels

Cited by 6 publications

References 36 publications

Two-dimensional inverse problem of fire location in the closed goaf of coal mine based on optical fiber sensors

Two-dimensional inverse problem of fire location in the closed goaf of coal mine based on optical fiber sensors

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

A novel unmanned aerial vehicle driven real-time situation awareness for fire accidents in chemical tank farms

Contact Info

Product

Resources

About