Abstract:In the urban rail signalling engineering project， signalling equipment suppliers need to allocate the overall operation safety target set by the owner to each subsystem of signalling system. At first， the system architecture， safety function， and safety target of the signalling system was analyzed. Then， the specific requirements and principles of the three designed safety target allocation methods were discussed. Considering the system architecture， safety control logic， and specific engineering construction parameters， three different allocation methods were used to allocate the overall system safety target to each subsystem. Finally， the allocation results calculated via the three different allocation methods were compared. The results obtained from the three allocation methods are in the same order of magnitude， which are all feasible. However， from the perspective of system safety， the allocation method based on system complexity is the preferable one， which is more suitable for urban rail system design and manufacturing.

Abstract:Aiming at the problem of obstacle detection in front of rail transit train， this paper proposes an obstacle detection algorithm based on LiDAR and considering laser reflection intensity for fully automatic train. This algorithm uses the Euclidean clustering method to cluster the point clouds， and combines the adaptive threshold processing of reflection intensity， VGF， clustering radius differentiation and other methods to improve the speed and accuracy of obstacle detection. The experiments show that the algorithm has a good detection performance.

Abstract:Aimed at the problem that the train loses satellite signal and can not be located， a dynamic time warping （DTW） based train smoothness information matching algorithm is proposed. With the help of specific sensor， the online regularity information of the train is collected. Afterward， the collected data is processed and matched by applying DTW-KF （Kalman filter）. Using the data measured by the onboard speed sensor as template variables， the online regularity measurement data is applied to do the matching. According to the optimal matching path， distortion， and matching results of DTW， the train can be positioned offline when the train cannot be located due to the loss of the satellite signals. The template data is divided into blocks （even division and quartile） to match in real-time， and the error correction of the INS （inertial navigation system） based on the matching result of the DTW algorithm can locate the train when the satellite signal loses online location information.

Abstract:Based on the fourth Panama Canal Bridge， the seismic relative displacement responses and control of the main-approach bridge of the rail-cum-road cable-stayed bridge were studied. Finite element analysis models of floating aseismic design， tower-girder consolidation aseismic design and consolidation aseismic design were developed， and the relative displacement responses of the three design schemes were computed. The effects of the differences in nature periods and damping characteristics of two adjacent single-degree-of-freedom systems on their relative displacements were analyzed. In combination with theoretical and parameter analyses， the control method on the relative displacement of the main-approach bridge was investigated. The results show that period ratios of the first longitudinal modes， the equivalent damping ratios and the damping mechanism of the main bridge and the approach bridge have significant influences on the relative displacement of adjacent structures， and the period ratio is the most influential factor. Different arrangements of the viscous dampers have different controlling effects on the relative displacement of the main-approach bridge. Fixing all viscous dampers between the girder ends of the main-approach bridges could enhance the synchronicity of the motion of the two adjacent structures and consequently obtain the best controlling effects. Simultaneously using some dampers on the main bridge piers and the others at the connection of the girders could benefit the configuration design and installation of the system， although the reduction of the relative displacement is a little lower.

Abstract:Alignment measurement is necessary during bridge construction. However， traditional measurement methods consume a lot of time and labors， especially for long-distance measurement. Unmanned aerial vehicle （UAV） photogrammetry has the advantages of high maneuverability， low cost， and independence on labor. The application of UAV to bridge alignment measurement can improve efficiency and promote construction control automation. In this paper， the framework and control parameters using UAV were studied as well as the layout of markers and route planning suitable for construction site. An experimental study was conducted to investigate the influence of parameters on the measurement accuracy including flying height， angle of bundle， photo overlap and quantity， based on which， the optimization method using UAV in bridge alignment survey was proposed. With the optimized measurement scheme， the coordinate measurement accuracy of each point can reach the millimeter level.

Abstract:Visible tests of homogeneous slope reinforced with anti-slide piles with different anchorage lengths were conducted by using transparent soil technique and particle image velocimetry （PIV）， to investigate the evolution of internal displacement field and the failure process of stabilized slope and reveal the effect of anchorage length on slip surface and anti-slide capacity of piles. The results show that with the increase in anchorage length， the slip surface of the reinforced slope gradually deepens and hence the resistance against sliding of the piles increases. However， when the anchorage length exceeds the optimal length， the soil behind the piles move over the pile top and the slip surface gets shallow， which results in the significant decrease of resistance of the anti-slide piles. As the anchorage length of pile is equal to the optimal length， two slip surfaces develop in the slope： a shallow one exiting above the pile top， and a deep one passing through the lower part of the pile. With the increase of anchorage length， the tensile zone of the outside wall in the upper section of the pile narrows but that of the inside wall in the lower section of the pile expands. Meanwhile， the maximum bending moments occur at a pile depth of about 0.4 times the total pile length. However， when the anchorage length is larger than the optimum length， the outside wall in the lower portion of the pile is subjected to tension whereas the inside wall bears tensile force in the upper portion of the pile. At the same time， the maximum bending moment occurs at a pile depth of about 0.35 times the total pile length. The optimum anchorage length is related to the properties of slope materials， pile spacing and stiffness， etc. Therefore， a suitable anchorage length of anti-slide pile should be determined in the project design considering the influence factors mentioned above.

Abstract:In this paper， plane-strain trapdoor tests were conducted to investigate the effects of reinforcement on the soil arching development under localized static loading， by using analogical soil made of aluminum rods as the backfill and by using four different reinforcement arrangements. Test results show that the use of reinforcement significantly minimizes soil arching under self-weight and localized static loading. Soil arching with reinforcement degrades greatly in unloading stages due to the existence of reinforcement， which was different from that without reinforcement. When the total reinforcement stiffness is the same， two low stiffness reinforcement layers with a backfill layer of certain thickness in between promotes soil arching. A soil arching analytical solution was proposed to predict the additional vertical stresses induced by localized static loading.

Abstract:In this paper， an evaluation index for the muck status inside chambers based on shear plate torque is proposed. Then multiple sets of model tests are then conducted to reveal the change characteristics of this index before and after the clogging of clay muck inside the chamber. Finally， a real-time method for judging the muck clogging is established. The results show that the tendency of muck adhesion gradually develops toward the cutter head after the difference appears in the angular velocity of the muck around the central axis near the partition. At the same time， muck empties at the top of the chamber. Muck blockage eventually occurred. The occurrence of the void zone is the preliminary stage of the muck blockage and can be identified by the difference between shear plate torques on the top and underside of the shield chamber.

Abstract:In view of the special topography， geology， construction status and location value of underground space construction in the core area of mountainous cities， a basic unit of 3D fine evaluation is used to divide urban underground space， the analytic hierarchy process is used to determine the suitability evaluation factor system， and a sensitive factor-fuzzy-variable weight composite model is established as the evaluation calculation method. The three-dimensional evaluation of the suitability of underground space development in the core area of mountainous cities is realized， which can provide guidance and reference for the 3D planning of underground space development. Taking the Yuzhong Peninsula of Chongqing as an example， the 3D divisions of the suitability of the 0 to 150 m underground space and the changes in the proportion of depth-suitability divisions are obtained. In addition， a typical block is selected to verify the reliability of the evaluation method.

Abstract:This paper proposes to integrate the theories and methods of artificial intelligence and cyber-physical fusion systems into intelligent transportation systems and their applications， and explore the cross-innovative fields of “artificial intelligence + transportation”， in terms of transportation physics and information space mapping， network communication and spatiotemporal data protocols. It forms relevant solutions and technical systems on key issues such as traffic safety and credible and controllable associations to promote the precise management of traffic information and physical integration systems and their applications.

Abstract:Based on the automatic fare collection （AFC） data， automatic train supervision （ATS） data， and the passenger walking time data， travel time thresholds of train choices as well as the influence factors are analyzed in detail. Then a hierarchy deduction model of passengers’ train choices is developed. The model is able to accurately deduce passengers’ train choices one by one， Case studies on the Beijing metro network verifies the good performance of the model.

Abstract:To deepen researches on metro passenger travel behavior under abnormal operating conditions， a prediction model of different passengers' tolerable waiting time under the metro train delay condition based on the survival analysis theory was proposed. First， the passenger tolerable waiting time was defined， and its characteristics were analyzed. Then， a parameter model based on Weibull distribution was selected to build the passenger tolerable waiting time prediction model considering various influencing factors. Finally， the proposed model was calibrated by the data obtained from a survey in combination with revealed preference and stated preference. The results show that the willingness of passengers to give up waiting and choose to leave the station increases along with the waiting time. Moreover， the passenger tolerable waiting time is positively correlated with the use frequency of metro per week， the metro travel time of the daily trip， the buffer time of the whole trip， and the metro travel time of the current trip. Compared with traveling for entertainment or other purposes， passengers have less tolerable waiting time when commuting. The probability that the tolerable waiting time of metro-oriented commuters exceeds 5.4 min and 14.3 min is over 80 % and below 50 %， respectively.

Abstract:In order to characterize the en-route charging behavior of battery electric vehicle （BEV） users， the location identification algorithm for identifying the en-route charging event was proposed and the K-means clustering method， including： the initial remaining power， the mileage after the last charging， the charging time and the charging speed， was used to classify the latent en-route charging behavior of BEV users and extract the typical en-route charging preference behavior characteristics. Based on the empirical longitudinal data of 300 BEVs with a battery range of about 150 km， the en-route charging behavior of BEV users are clustered into： the low anxiety and fast charging mode， the high anxiety and fast charging mode， the anytime charging mode and the destination charging mode. BEV users prefer the fast charging mode. The research results provide technical support for the scientific and rational deployment of charging stations.

Abstract:In order to accurately predict the cooling load of metro stations， firstly， by analyzing the historical data from the metro station monitoring platform， the passenger flow and meteorological parameters are identified as the main influential factors. Then， a dynamic passenger flow neural network prediction model is established， by utilizing the hourly monitored carbon dioxide volume concentration data in the metro station， A comparion of the carbon dioxide data with the automatic fare collection data indicates that the correlation coefficient R² of the prediction model can reach 0.87. After that， based on passenger flow prediction， the cooling load predicting model of the metro station is built. A comparsion of the predicting performance of back propagation neural network and support vector machine algorithm under different time scale training data suggests that the R² of both models can reached 0.95 or higher， and the root mean square error is between 70 kW and 90 kW， which is of a high prediction accuracy. However， the calculation time of the support vector machine algorithm is about 3 to 4 times that of the back propagation neural network algorithm. The neural network model is recommended when the amount of data is large.

Abstract:To improve the service efficiency of the hydrogen refueling station and reduce the infrastructure investment and scientifically select the station location， according to the particularity of highway travel， a one-dimensional road hydrogen station location model on expressway is established. First， the economic model of the hydrogen refueling station is introduced to refine economic analysis. Then， the location and filling capacity of the hydrogen refueling station are optimized considering the influence of boundary conditions and randomness， and the hydrogen supply scheme is given. In terms of algorithm， the location combinations of hydrogen refueling stations were enumerated， and the filling capacity problem was converted into a linear programming problem to solve. Finally， taking the G15 expressway in the Yangtze River Delta as an example， the location layout of hydrogen refueling stations is conducted to construct the intercity hydrogen refueling station network.

Abstract:To meet the demands of crack detection market in domestic tall buildings， taking the shortcomings of the fact that the existing YOLOv4-tiny deep network structure runs slowly on such edge devices as Raspberry Pi into account， two novel simplified YOLOv4-tiny deep network structures， that is， YOLOv4-lite1 and YOLOv4-lite2 were deduced by removing the second residual network， as well as adding a maxpool layer and changing the connection of the last route layer in this paper. The training set， the test set， and the verification set data of crack detection were then generated by using the crack pictures downloaded from the internet， and the training is conducted on a 64-bit Ubuntu16.04 system utilizing the Darknet deep learning framework. At the same time， the actual tests on the RaspberryPI 4B show that the YOLOv4-lite1 structure has a faster running speed， detection rate， and stability compared to the YOLOv4-lite2 structure. Finally， the next step of this related work is pointed out. The innovation of this research lies in further simplifying the YOLOv4-tiny network structure and the connection of the last layer route layer， thus obtaining two new YOLOv4-tiny deep network structures and better detection results.

Abstract:In the construction of a super-tall structure， robotic total station （RTS） is more suitable for monitoring the deformation of points in the middle floors. Reducing the impact of building shaking is critical to the stability of monitoring results. This paper introduces a method of using roof navigation satellite receiver to monitor the vibration of the roof， and then interpolates roof results to get the impact of instantaneous shaking of middle floors， thus eliminating the impact of building shaking on RTS monitoring results. Then， the effectiveness of this method is verified by experiments. Multi-phase monitoring data are processed by using this method. The results show that this method can obtain more stable monitoring results. A comparison of the results of measurement with those of finite element analysis shows that the monitoring results is more consistent with the finite element analysis results.