Adjacent construction around metro tunnels is a common situation in urban cities, which usually leads to the disturbance of surrounding soils and existing tunnels. Based on the influence zone theory, construction risks can be described in a graded manner. However, the calculation of the influence zone in existing research is mostly based on a single indicator, which is difficult to adapt to the increasingly stringent requirements of real projects. To address this limitation, a normalized method for the influence function and a weighted combination method for multi-indicators were proposed. These methods can combine any number of indicators to obtain an integrated result of the influence zone, facilitating direct guidance of engineering. Based on a case study of the Metro construction in Qingdao, a series of numerical simulations and calculations of the integrated influence zone was carried out. The results show that the shape of the integrated influence zone in this case is a necked bottle, i.e., the new tunnel causes a greater influence when it is below the existing tunnel. The change of soil layer and the weight of indicators both play important roles on the shape of the influence zone. The relevant conclusions and methods provide a basis for the safety assessment of similar projects.

Track displacement is an important factor of track irregularity. Existing researches related with track displacement prediction generally ignore the influence from underground construction engineering such as shield tunneling, resulting in inaccurate estimation of track displacement. To fill this gap, we propose a three-stage framework to predict the track displacement when the shield tunnel under crosses the existing tunnel. Firstly, by considering the curved shield tunneling, a three-dimensional model is constructed to estimate the total ground displacement during the whole tunneling process. Furthermore, the soil-tunnel interaction model is established to estimate the deformation of the existing tunnel. To tackle the issue of unknown node displacements, cubic splines are used to interpolate the unknown values of tunnel displacements. On this basis, the direct stiffness method is used to estimate the track displacement and to calculate the track irregularity. Finally, the effectiveness of the proposed method is verified and the prediction performance on the track irregularity is evaluated using a real engineering case and the finite element simulation. The main contributions of this article lie in the modeling of the curved scenario for the estimation of the ground loss, as well as the combination of cubic splines and direct stiffness method, which improve the accuracy of the track displacement estimation during shield tunneling.

Based on a cross-river tunnel of Wuhan Metro Line 8, we present a two-dimensional discrete element model for shield attitude adjustment considering the effect of overbreak cutters. The shield shell mechanics under the influence of over-excavation rate, over-excavation orientations, and overburden load are simulated, and the tunneling mechanics law and the ultimate range during the adjustment of the shield attitude are investigated. The simulation results indicate the following: (1) The greater the over-excavation rate, the smaller the force exerted by the soil layer in the negative direction of the shield movement; therefore, increasing the over-excavation rate is helpful in expanding the range of shield attitude adjustment. (2) The shield is stressed symmetrically while conducting positive and negative horizontal adjustments in the soil layer, which has a symmetrical distribution, but vertical upward adjustment is more difficult than vertical downward adjustment. (3) With the increase in overburden load, the space of the shield attitude adjustment is gradually reduced at the same over-excavation rate. A good engineering application was achieved in this project using the simulation model. It is recommended to use the attitude adjustment method by controlling the tunneling parameters. In difficult situations such as high overlying loads, the over-excavation cutter can be used to assist in adjusting the shield attitude.

Thorough evaluation of suburban rail route alternatives is essential for determining the appropriate route. Suburban lines provide transportation services to suburban areas; that is, they provide transportation services to distant points of the city where demand is high. In this study, five alternative suburban rail routes in the commuter rail standard, starting from the Fatih Sultan Mehmet Bridge’s European exit and ending at Istanbul Airport, were determined as a sample corridor in the province of Istanbul. High-standard suburban rail routes and their infrastructure construction costs were determined, and route options were delineated according to their construction costs. Multi-criteria decision-making (MCDM) methods were examined, including the application of the decision matrix method (DMM) and analytical hierarchy process (AHP) to rank the routes in terms of the factors examined in the study. These routes were ranked according to the construction costs and according to the DMM and AHP methods, and the results were compared. In the decision matrix application, geomorphological-geological, geotechnical, infrastructural, and environmental factors were considered in the evaluation and determination process. A solution was developed in which all aspects were weighted to indicate their relevance, and these factors contributed to the determination of the results. The AHP method was also applied, in which five criteria were included in the evaluation: geology (including geomorphology and soil properties), engineering structures, construction costs, population values addressed by the routes, and railroad length. In addition, the distances to the active faults were examined according to the Turkey Earthquake Hazard Map specified in the Turkish Building Earthquake Code (TBDY) 2018. Depending on the factors studied, the ordering of the routes was the same for all three methods.

With the accelerated operation of subway networks, the increasing number of subway transfer stations results in inefficient passenger travel. The target of this paper is to solve the research question of how to reduce transfer waiting time (TWTT) for heterogeneous passengers. The key problem is to determine the optimal concerted train timetable considering the transfer walking time (TWKT) of the passengers. On the basis of field survey data, the regression method was used to establish a TWKT prediction model for general passengers (G) and vulnerable passengers (V), including the elderly, passengers traveling with children, and those carrying large luggage. Afterward, a two-objective integer programming model was formulated to minimize the subway operating costs and TWTT for each group, in which V is given the priority weight to ensure social equity. The headway, loading capacity, and TWKT of heterogeneous passengers were set as optimization model constraints. A genetic algorithm (GA) was designed to find the optimal solution. A case study in which the Beijing Jianguomen Station was selected as the key transfer station was conducted to verify the performance of the proposed model. Key results show that the total TWTT for V and G can be reduced by 18.6% and 27.2%, respectively, with one train saved. Results of the parameter sensitivity analysis reveal the interconnection between the operating cost, heterogeneous passenger proportion, and transfer time. The proposed model can be used for improving transfer efficiency for passengers while considering the enterprise operating costs.

Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research on the duration of the disruption impacts on passenger trips is limited given that the duration is not observed directly. This paper proposes a probabilistic method to estimate the disruption impact duration using smartcard data, explores statistical and machine learning models to predict the duration of impacts on passengers, and identifies influencing factors including incident characteristics, operating conditions, infrastructure, external factors, and demand. The results highlight that prediction accuracies are acceptable for multiple linear regression, accelerated failure time, and random forest models. Disruptions caused by power failures have longer impact durations than other causes, followed by platform screen doors. The fixed block signaling system leads to a larger disruption duration than the moving block system. The study provides, for the first time, a data-driven approach to understanding the duration of the impact of disruptions on passenger trips using smartcard data which can facilitate timely and informed decision-making under unplanned disruptions.