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

2018-09-01
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.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT

Suggestions

Detailed load rating analyses of bridge populations using nonlinear finite element models and artificial neural networks
Hasançebi, Oğuzhan (2013-11-01)
For assessing load rating capacity of bridges, American Association of State Highway and Transportation Officials Manual (AASHTO) recommends a simple method, where distribution of the forces in transverse direction is estimated by axle-load distribution factors on a simply supported beam. Although the method is practical in the sense that it allows for rapid evaluation of bridge populations, it leads to over-conservative load ratings. A finite element (FE) based load rating analysis is conceived as a more a...
Prediction of the maximum air velocities created by metro trains using an artificial neural network approach
KOC, Gencer; Sert, Cüneyt; Albayrak, Kahraman (2014-09-01)
The maximum air velocity created by a moving train inside a tunnel is obtained using an artificial neural network approach. A neural network model is developed to represent a single train travelling in a single tunnel. A set of non-dimensional groups, which are known to influence the induced flow characteristics, is used for the training of the neural network. Various test runs are compared with the results of the authoritative software, Subway Environmental Simulation. The presence of ventilation shafts wi...
Underground transportation system ventilation by train piston effect
Aradağ, Selin; Eralp, O.Cahit; Department of Mechanical Engineering (2002)
In this study, the general points of subway ventilation are given, focusing on "Train piston action ventilation". A computer program has been developed to simulate train piston action in underground transportation systems. The program has been named as Trapac (Train piston action). Pressure and velocity distributions are computed along tunnel direction and in time, when a train is passing through a tunnel. inThe partial differential equations that govern the unsteady flow of air in the tunnel are transforme...
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 ...
Dynamic effects of moving traffic on railway bridges
Cinek, Fatih; Yılmaz, Çetin; Department of Civil Engineering (2010)
In this study, dynamic effects on high speed railway bridges under moving traffic are investigated. Within this context, the clear definition of the possible dynamic effects is provided and the related studies that exist in literature are investigated. In the light of those studies, analytical procedures that are defined to find the critical dynamic responses such as deflections, accelerations and resonance conditions are examined and a MatLab programming language is written to obtain the responses for diff...
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: https://hdl.handle.net/11511/32005.