Temperature data-driven fire source estimation algorithm of the underground pipe gallery

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.