Experimental investigation on the invert stability of operating railway tunnels with different drainage systems using 3D printing technology

In recent years, the invert anomalies of operating railway tunnels in water-rich areas occur frequently,which greatly affect the transportation capacity of the railway lines. Tunnel drainage system is a crucial factor to ensure the invert stability by regulating the external water pressure(EWP). By means of a threedimensional(3D) printing model, this paper experimentally investigates the deformation behavior of the invert for the tunnels with the traditional drainage system(TDS) widely used in China and its optimized drainage system(ODS) with bottom drainage function. Six test groups with a total of 110 test conditions were designed to consider the design factors and environmental factors in engineering practice,including layout of the drainage system, blockage of the drainage system and groundwater level fluctuation. It was found that there are significant differences in the water discharge, EWP and invert stability for the tunnels with the two drainage systems. Even with a dense arrangement of the external blind tubes, TDS was still difficult to eliminate the excessive EWP below the invert, which is the main cause for the invert instability. Blockage of drainage system further increased the invert uplift and aggravated the track irregularity, especially when the blockage degree is more than 50%. However, ODS can prevent these invert anomalies by reasonably controlling the EWP at tunnel bottom. Even when the groundwater level reached 60 m and the blind tubes were fully blocked, the invert stability can still be maintained and the railway track experienced a settlement of only 1.8 mm. Meanwhile, the on-site monitoring under several rainstorms further showed that the average EWP of the invert was controlled within 84 k Pa, while the maximum settlement of the track slab was only 0.92 mm, which also was in good agreement with the results of model test.

Robust train speed trajectory optimization: A stochastic constrained shortest path approach

Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete samplebased random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.

Train delay analysis and prediction based on big data fusion

Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport,train delays often occur.For this study,a three-month dataset of weather,train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time.We found that in severe weather train delays are determined mainly by the type of bad weather,while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains.Identifying the factors closely correlated with train delays,we developed a machine-learning model to predict the delay time of each train at each station.The prediction model is useful not only for passengers wishing to plan their journeys more reliably,but also for railway operators developing more efficient train schedules and more reasonable pricing plans.