Since there exists few effective fire detection technologies for underground pipe gallery application, a temperature data-driven bio-inspired artificial intelligence algorithm is developed to detect fire source in 3D space of the underground pipe gallery, in which a simple physical model is used. In the developed algorithm, Ant colony optimization (ACO) is the first time to be used to determine tunnel fire source, and the new and special pheromone evaporation method and heuristic factor are developed for fitting the concerned problem here. Three fire experiments are used to support the ability of the algorithm. Satisfactory results can always be obtained, which shows that the developed algorithm can be used to estimate the tunnel fire source as well as temperature prediction. In addition, since only temperature data at several sensors is necessary in the developed algorithm, it has a very wide popularization and engineering application prospects due to its advantages of the global optimal ability and computational efficiency as well as the low economic cost.

针对地下管廊应用中有效的火灾探测技术较少，开发了一种基于温度数据驱动的仿生人工智能算法来探测地下管廊3D空间中的火源，并使用简单的物理模型。在开发的算法中，首次将蚁群优化（ACO）用于确定隧道火源，并开发了新的特殊信息素蒸发方法和启发式因子来拟合这里的相关问题。三个火灾实验用于支持算法的能力。总能得到满意的结果，表明所开发的算法可用于隧道火源估计和温度预测。此外，