Use of artificial neural networks for the prediction of time-dependent air speed variation in metro stations

In this study, the time-dependent, induced air speeds at critical sections of underground metro stations are assessed using a novel one-dimensional data-driven approach. For this purpose, three artificial neural networks are used, each trained for the most basic configuration of a single train moving in a single tunnel. The first two are trained to provide the maximum and time averaged values of the induced air speeds while the train is moving inside the approach tunnel or the station. The third one is used to simulate the time-dependent air speed variation during train stoppage and departure. Typical structures of a metro system such as staircases and ventilation shafts are introduced into the solution using simple analytical relations based on loss coefficients. The developed approach is tested using two different metro stations that are currently in operation in Turkey. The selected stations are constructed using different tunneling techniques resulting in different air flow characteristics. The results show that the time variation of the air speed predicted by the developed model is, in general, in good agreement with the results of the Subway Environmental Simulation software, although further studies are necessary to model the acceleration and deceleration of trains more realistically.


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Utilization of neural networks for simulation of vehicle induced flow in tunnel systems
Koç, Gencer; Albayrak, Kahraman; Sert, Cüneyt; Department of Mechanical Engineering (2012)
Air velocities induced by underground vehicles in complex metro systems are obtained using artificial neural networks. Complex tunnel shaft-systems with any number of tunnels and shafts and with most of the practically possible geometries encountered in underground structures can be simulated with the proposed method. A single neural network, of type feed-forward back propagation, with a single hidden layer is trained for modelling a single tunnel segment. Train and tunnel parameters that have influence on ...
Citation Formats
G. KOC, C. Sert, and K. Albayrak, “Use of artificial neural networks for the prediction of time-dependent air speed variation in metro stations,” PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT, pp. 2186–2197, 2018, Accessed: 00, 2020. [Online]. Available: