Sparse Learning-Based Optimal Design for Tractive Layout of Railway Turnout

Railway turnouts are critical but weak devices that branch one track into two or more. To ensure the safety of train passage, the insufficient displacement (ID) of the switch rail must be controlled within the deviation required by the railway engineering. A reasonable tractive layout is the key to maintaining the ID of the switch rail at a relatively low level after its operation. Existing research generally formulates the design problem as a non-convex problem and cannot guarantee global convergence. To tackle this issue, this paper proposes a sparse learning-based design method for the tractive layout of the switch rail. Firstly, by defining the tractive force vector as the indicator variable, we transform the original combinatorial optimization problem into a continuous one and formulate a constrained convex problem by combining the$\boldsymbol {L}_{\mathbf {1}}$norm and the weighted squared ID. Then, an ADMM-based algorithm is developed to efficiently solve the force vector, thereby adaptively selecting the tractive layout. Finally, we validate the effectiveness of the proposed method using two typical turnouts in railway industry.