The design and planning of railway alignments is the dominant task in railway construction. However, it is difficult to achieve self-learning and learning from human experience with manual as well as automated design methods. Also, many existing approaches require predefined numbers of horizontal points of intersection or vertical points of intersection as input. To address these issues, this study employs deep reinforcement learning (DRL) to optimize mountainous railway alignments with the goal of minimizing construction costs. First, in the DRL model, the state of the railway alignment optimization environment is determined, and the action and reward function of the optimization agent are defined along with the corresponding alignment constraints. Second, we integrate a recent DRL algorithm called the deep deterministic policy gradient with optional human experience to obtain the final optimized railway alignment, and the influence of human experience is demonstrated through a sensitivity analysis. Finally, this methodology is applied to a real-world case study in a mountainous region, and the results verify that the DRL approach used here can automatically explore and optimize the railway alignment, decreasing the construction cost by 17.65% and 7.98%, compared with the manual alignment and with the results of a method based on the distance transform, respectively, while satisfying various alignment constraints. INTRODUCTIONThe planning and design of railway alignments is not only the foundation of railway construction but also an extensive and systematic task. The direction of a railway alignment directly affects the difficulty, cost, and safety of the railway construction and operation. Therefore, the final railway alignment design should not only consider a series of natural factors such as geology and topography in the © 2021 Computer-Aided Civil and Infrastructure Engineering railway area but also satisfy other constraints, including those regarding the existing railway, historical sites, and environmental protection zones in the target area. Overall, pathfinding for a railway alignment is an optimization and decision-making problem involving many restrictive factors (Li et al., 2013). Traditional railway path planning is performed manually. Based on work experience and accumulated knowledge, designers analyze, evaluate, and compare multiple Comput Aided Civ Inf. 2022;37:73-92.wileyonlinelibrary.com/journal/mice ing requirements, thus ensuring that the horizontal alignment satisfies the constraints in the horizontal plane. In this study, we set 𝑅 𝑖 as a fixed value, which should exceed the minimum allowed value of 600 m (specified in Table 1), to fit the horizontal circular curve.

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Rail wear occurs continuously owing to the rolling contact load of trains and is fundamental for railway operational safety. A point‐based manual rail wear inspection cannot satisfy the increasing demand for rapid, low‐cost, and continuous monitoring. This paper proposes a depth‐plus‐region fusion network for detecting rail wear on a running band, which is a collection of wheel–rail interaction traces. The following steps are involved in the proposed method. (i) A depth map estimated by a modified MiDaS model is utilized as guidance for exploiting the depth information of the running band for rail wear detection. (ii) The running band of a rail is segmented and extracted from images using an improved mask region‐based convolutional neural network that uses the scale and ratio information to perform instance segmentation of the running band images. (iii) A two‐channel attention–fusion network that classifies rail wear is constructed. In this study, we collected real‐world running band images and rail wear‐related data to validate our approach using a high‐accuracy rail‐profile measurement tool. The case‐study results demonstrated that the proposed method can rapidly and accurately detect rail wear under different ambient light conditions. Moreover, the recall rate of severe wear detection was 84.21%.

Understanding the relationship between the static and dynamic track geometry irregularities is crucial for the proper maintenance of rail infrastructures and the reduction of on-site workload. This paper focuses on the analysis of the dynamic and static track irregularities on simply-supported beam bridges for high-speed railways. Based on the simulation of three-dimensional vehicle–track–bridge dynamics, a virtual track inspection method is proposed according to the measurement principle with the inertial reference. With the static irregularity provided as the initial input to the simulation model, the virtual track inspection of dynamic track irregularities is carried out considering different supporting structures, i.e. subgrade and bridges. Furthermore, the characteristics and advantages of the proposed model are investigated in the “rigid track structure”. Then, using the virtual track inspection method, this paper analyzes the relationship between the dynamic and static track irregularities (in the vertical direction) on the simply-supported beam bridge in both the time and frequency domains with respect to different train speeds, and the simulation results are validated by real-world measurements. Numerical results show that the stiffness irregularity in the vertical direction is periodical, with the cycle length equal to the span of the bridge. Furthermore, there is an obvious linear relationship between the dynamic and static irregularities. Also, the regression coefficient increases with increasing vehicle speed.

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This paper presents a geometric wheel–rail contact algorithm suitable for fault detection in a railway turnout. It employs a dynamic profile sampling method based on the characteristics of the variable section rails in the turnout regions. This algorithm provides a fast and accurate method to identify the wheel–rail contact points in turnouts under different degrees of lateral shift and yaw angles. This method determines the contact point based on the minimum distance and can be applied quickly through the use of a moving window to scale the potential contact boundary within the rail width. The wheel–rail contact points and the geometric contact parameters in the internal rail that matched with the LMa and 60 N were virtually identical in both methods, which verifies the accuracy of the dynamic profile sampling model. A comparison of the different wheel–rail contact points and geometric parameters under different wheelset yaw angles in turnouts using the trace line and dynamic profile sampling methods shows that a trace method based on the assumption of equal rail sections is not applicable to turnouts, thus demonstrating the necessity of calculating the wheel–rail contact geometry in turnouts using the dynamic profile sampling method, which takes the characteristics of the variable sections into account.