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Keywords = high-speed railway

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18 pages, 522 KiB  
Article
Towards a Low-Carbon Target: How the High-Speed Rail and Its Expansion Affects Industrial Concentration and Macroeconomic Conditions: Evidence from Chinese Urban Agglomerations
by Minhua Yang, Rui Yao, Linkun Ma and Ang Yang
Sustainability 2024, 16(19), 8430; https://doi.org/10.3390/su16198430 (registering DOI) - 27 Sep 2024
Abstract
High-speed rail is a high-standard railway system, which allows trains to operate at high speed. The railway play a crucial role in connecting urban agglomerations, which represents the highest form of spatial organization in the mature stage of urban development, bringing together cities [...] Read more.
High-speed rail is a high-standard railway system, which allows trains to operate at high speed. The railway play a crucial role in connecting urban agglomerations, which represents the highest form of spatial organization in the mature stage of urban development, bringing together cities of various natures, types, and scales in specific regions. This paper explores the impacts of high-speed rail and its expansion on industrial concentration and macroeconomic conditions in the period of 2000 to 2019. We use a well-known transportation policy as a natural experiment, utilizing geographic distance data to study the effects of high-speed rail and its expansion on industrial concentration and macroeconomic conditions in urban agglomerations. The results show that high-speed rail increases industrial concentration but leads to a reduction in macroeconomic conditions. Unlike previous studies in this field, we use distance variables to analyze how the expansion of high-speed rail affects macroeconomic conditions and industrial concentration through location advantages. The impacts of high-speed rails vary across urban and non-urban agglomeration cities, resource-based and non-resource-based cities, large and small cities, and eastern, central, and western regions. Our results are robust to the shocks from the global financial crisis, time lags, different distance dummy variables, dependent variables, and endogeneity issues. This study regards the opening up of high-speed rail as both improving air quality and reducing carbon emissions through substituting for urban and aviation transport. Compared to traditional transport methods such as urban and air travel, the efficiency and environmental benefits of high-speed rail make it an important method for reducing greenhouse gas emissions. Consequently, the expansion of high-speed rail could support both economic development and environmental concerns, and it is playing a crucial role in transportation selection for advancing low-carbon economic goals. Full article
(This article belongs to the Special Issue Digitalization and Its Application of Sustainable Development)
19 pages, 6390 KiB  
Article
Study on Dynamic Response Characteristics and Monitoring Indicators of High-Speed Railway Subgrade in Karst Areas
by Mingzhou Bai, Ling Yang, Yanfeng Wei and Hongyu Liu
Appl. Sci. 2024, 14(19), 8715; https://doi.org/10.3390/app14198715 (registering DOI) - 27 Sep 2024
Abstract
The impact of karst collapses on railway engineering spans the entire lifecycle of railway construction and operation, with train loads being a significant factor in inducing such collapses. To study the dynamic response characteristics of subgrades in karst areas and to select appropriate [...] Read more.
The impact of karst collapses on railway engineering spans the entire lifecycle of railway construction and operation, with train loads being a significant factor in inducing such collapses. To study the dynamic response characteristics of subgrades in karst areas and to select appropriate monitoring points and indicators for long-term effective monitoring, a numerical simulation method was employed to analyze the vibration response characteristics of the subgrade. A three-dimensional finite element model coupling the high-speed train, ballastless track, and subgrade foundation was established to study the vibration responses of subgrades when the train passes over a subgrade with an underlying soil hole and one without a soil hole. The results indicate that when there was a soil hole, both the dynamic displacement amplitude and vibration acceleration amplitude decreased, while the dominant frequency slightly increased, with the dominant frequency being higher at locations closer to the soil hole. The vibration response at the soil hole location showed significant attenuation, with the attenuation coefficient of dynamic displacement amplitude being higher than that of the vibration acceleration amplitude. Monitoring points were arranged at positions 0 m to 10 m from the toe of the slope, with vertical dynamic displacement, vertical vibration acceleration, the dominant frequency of vertical vibration acceleration, and corresponding amplitude selected as monitoring indicators. These indicators effectively reflect whether soil holes exist within the subgrade and help identify the locations of defects. This study summarizes the dynamic response characteristics of subgrades in karst areas under different conditions, providing a basis for the design and monitoring of railway subgrades in regions prone to karst collapse. Full article
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<p>Simplified diagram of the vehicle model.</p>
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<p>3D Simulation diagram of the vehicle-track-subgrade.</p>
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<p>Equipment used for field monitoring: (<b>a</b>) Model 291-2 vibration picker, (<b>b</b>) Data-acquisition instrument.</p>
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<p>Distribution diagram of field monitoring points(Point 1: Outside the fence; Point 2: 5 meters from Point 1; Point 3: 10 meters from Point 1; Point 4: 15 meters from Point 1).</p>
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<p>Comparison of vibration acceleration between field monitoring and numerical simulation.</p>
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<p>Comparison of vibration acceleration amplitude between field monitoring and numerical simulation.</p>
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<p>Layout of the monitoring points.</p>
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<p>Mesh division diagram of the soil hole.</p>
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<p>Time history curve of the dynamic displacement at different positions at the toe of the high-speed railway subgrade in karst areas.</p>
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<p>Displacement cloud diagram of the subgrade (unit: mm).</p>
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<p>The distribution characteristics of dynamic displacement amplitude in the subgrade of a high-speed railway in a karst area.</p>
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<p>Spatial distribution of vibration acceleration amplitude at measurement points on survey lines 1 and 3.</p>
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<p>Spectra of the vibration acceleration at different measuring points.</p>
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<p>The spatial relationship curves of the dominant frequency and corresponding amplitude at the measurement points on survey lines 1 and 3.</p>
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21 pages, 6080 KiB  
Article
Seismic Fragility Analysis of Reinforced Concrete Simply Supported Girder Bridges Resting on Double-Column Piers for High Speed Railway
by Yongzheng Zhou, Ce Gao, Sibo Yang, Wei Guo and Liqiang Jiang
Buildings 2024, 14(10), 3072; https://doi.org/10.3390/buildings14103072 - 26 Sep 2024
Abstract
This study investigates the probabilistic seismic damage characteristics of a five-span RC simply supported girder bridge with double-column piers designed for a high-speed railway (HSR). The objective is to assess the bridge’s fragility by developing a refined nonlinear numerical model using the OpenSEES [...] Read more.
This study investigates the probabilistic seismic damage characteristics of a five-span RC simply supported girder bridge with double-column piers designed for a high-speed railway (HSR). The objective is to assess the bridge’s fragility by developing a refined nonlinear numerical model using the OpenSEES (Version 3.3.0) platform. Incremental dynamic analysis (IDA) was conducted with peak ground accelerations (PGA) ranging from 0.05 g to 0.5 g, and fragility curves for pier columns, tie beams, and bearings were developed. Additionally, a series–parallel relationship and a hierarchically iterated pair copula model were established to evaluate system fragility. The results indicate that as PGA increases, the damage probability of all bridge components rises, with bearings being the most vulnerable, followed by pier columns, and tie beams exhibiting the least damage. The models accurately simulate the correlations between members and system fragility, offering valuable insights into the bridge’s performance under seismic conditions. Full article
(This article belongs to the Special Issue Recent Study on Seismic Performance of Building Structures)
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<p>Schematic diagram of the bridge layout (unit: m).</p>
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<p>Schematic diagram of the bearing layout.</p>
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<p>Finite element model of the bridge.</p>
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<p>Schematic diagram of the finite element model in Midas.</p>
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<p>Comparison between the acceleration mean spectrum of ground motions and the code spectrum.</p>
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<p>Curvature envelopes of the pier columns and tie beams.</p>
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<p>Bending moment–curvature diagrams of key sections for pier columns and tie beams. (<b>a</b>) Pier bottom section. (<b>b</b>) Pier and tie beam bottom section. (<b>c</b>) Section at both ends of tie beam.</p>
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<p>IDA curves of key sections for pier columns and tie beams.</p>
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<p>Probabilistic seismic demand models of the bridge components.</p>
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<p>Fragility curves of key sections for pier columns.</p>
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<p>Fragility curves of key sections for tie beams.</p>
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<p>Moment envelopes of P1 and P2 (excited by the RSN1338 ground motion).</p>
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<p>Peak axial force responses of the pier and tie beam bottom sections under different working conditions.</p>
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<p>Fragility curves of bearings. (<b>a</b>) Minor damage to fixed bearing and longitudinal movable bearing. (<b>b</b>) Moderate damage to fixed bearing and longitudinal movable bearing. (<b>c</b>) Severe damage to fixed bearing and longitudinal movable bearing. (<b>d</b>) Complete damage to fixed bearing and longitudinal movable bearing. (<b>e</b>) Minor damage to transverse, multidirectional movable bearing. (<b>f</b>) Moderate damage to transverse, multidirectional movable bearing.</p>
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<p>Schematic diagram of the series–parallel relations between members and the hierarchically iterated pair copula model.</p>
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<p>Fragility curves of the system.</p>
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<p>Comparison of fragility curves between the copula function method and first-order bound method.</p>
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17 pages, 3111 KiB  
Article
Transformer-Based High-Speed Train Axle Temperature Monitoring and Alarm System for Enhanced Safety and Performance
by Wanyi Li, Kun Xie, Jinbai Zou, Kai Huang, Fan Mu and Liyu Chen
Appl. Sci. 2024, 14(19), 8643; https://doi.org/10.3390/app14198643 - 25 Sep 2024
Abstract
As the fleet of high-speed rail vehicles expands, ensuring train safety is of the utmost importance, emphasizing the critical need to enhance the precision of axel temperature warning systems. Yet, the limited availability of data on the unique features of high thermal axis [...] Read more.
As the fleet of high-speed rail vehicles expands, ensuring train safety is of the utmost importance, emphasizing the critical need to enhance the precision of axel temperature warning systems. Yet, the limited availability of data on the unique features of high thermal axis temperature conditions in railway systems hinders the optimal performance of intelligent algorithms in alarm detection models. To address these challenges, this study introduces a novel dynamic principal component analysis preprocessing technique for tolerance temperature data to effectively manage missing data and outliers. Furthermore, a customized generative adversarial network is devised to generate distinct data related to high thermal axis temperature, focusing on optimizing the network’s objective functions and distinctions to bolster the efficiency and diversity of the generated data. Finally, an integrated model with an optimized transformer module is established to accurately classify alarm levels, provide a comprehensive solution to pressing train safety issues, and, in a timely manner, notify drivers and maintenance departments (DEPOs) of high-temperature warnings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Fault Diagnosis and Signal Processing)
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<p>The time-varying temperature of various axles in 2 data samples: (<b>a</b>) axle 1; (<b>b</b>) axle 2; (<b>c</b>) axle 3; (<b>d</b>) axle 4; (<b>e</b>) axle 5; (<b>f</b>) axle 6; (<b>g</b>) axle 7; (<b>h</b>) axle 8.</p>
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<p>Before data processing of the outlier axle temperature.</p>
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<p>After data processing of the outlier axle temperature.</p>
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<p>Flowchart of training the adversarial generative networks.</p>
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<p>Schematic representation of the optimization layer for the discriminant model.</p>
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<p>Comparison of data visualization of strong thermal features generated before and after optimization.</p>
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<p>Input to the prediction stage model.</p>
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<p>Input to the new prediction stage model.</p>
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<p>Variations of λ1, λ2, and λ3 during iteration process.</p>
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20 pages, 8403 KiB  
Article
Soil Displacement of Slurry Shield Tunnelling in Sandy Pebble Soil Based on Field Monitoring and Numerical Simulation
by Jian Cui, Zhigang Yao, Tao Yu, Jianfeng Wang, Kaichen Ying, Bo Liu, Shu Zhu and Xiaonan Yan
Buildings 2024, 14(10), 3043; https://doi.org/10.3390/buildings14103043 - 24 Sep 2024
Abstract
Due to its inherent advantages, shield tunnelling has become the primary construction method for urban tunnels, such as high-speed railway and metro tunnels. However, there are numerous technical challenges to shield tunnelling in complex geological conditions. Under the disturbance induced by shield tunnelling, [...] Read more.
Due to its inherent advantages, shield tunnelling has become the primary construction method for urban tunnels, such as high-speed railway and metro tunnels. However, there are numerous technical challenges to shield tunnelling in complex geological conditions. Under the disturbance induced by shield tunnelling, sandy pebble soil is highly susceptible to ground loss and disturbance, which may subsequently lead to the risk of surface collapse. In this paper, large-diameter slurry shield tunnelling in sandy pebble soil is the engineering background. A combination of field monitoring and numerical simulation is employed to analyze tunnelling parameters, surface settlement, and deep soil horizontal displacement. The patterns of ground disturbance induced by shield tunnelling in sandy pebble soil are explored. The findings reveal that slurry pressure, shield thrust, and cutterhead torque exhibit a strong correlation during shield tunnelling. In silty clay sections, surface settlement values fluctuate significantly, while in sandy pebble soil, the settlement remains relatively stable. The longitudinal horizontal displacement of deep soil is significantly greater than the transverse horizontal displacement. In order to improve the surface settlement troughs obtained by numerical simulation, a cross-anisotropic constitutive model is used to account for the anisotropy of the soil. A sensitivity analysis of the cross-anisotropy parameter α was performed, revealing that as α increases, the maximum vertical displacement of the ground surface gradually decreases, but the rate of decrease slows down and tends to level off. Conversely, as the cross-anisotropy parameter α decreases, the width of the settlement trough narrows, improving the settlement trough profile. Full article
(This article belongs to the Special Issue Structural Analysis of Underground Space Construction)
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<p>Ground movements induced by tunnelling.</p>
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<p>Settlement troughs defined by Gaussian distribution curve after Peck [<a href="#B22-buildings-14-03043" class="html-bibr">22</a>].</p>
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<p>Geological profile of Tsinghuayuan shield tunnel.</p>
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<p>Core photos: (<b>a</b>) depth of 40–45 m; (<b>b</b>) depth of 45–50 m.</p>
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<p>Typical strata monitoring section layout diagram.</p>
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<p>Distribution and arrangement of measuring points: (<b>a</b>) monitoring system of transverse surface settlement; (<b>b</b>) layout of surface settlement monitoring points; (<b>c</b>) layout of horizontal displacement.</p>
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<p>Field test inclinometer.</p>
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<p>Main tunnelling parameters: (<b>a</b>) shield thrust; (<b>b</b>) cutterhead torque; (<b>c</b>) slurry pressure; (<b>d</b>) tunnelling speed.</p>
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<p>Main tunnelling parameters: (<b>a</b>) shield thrust; (<b>b</b>) cutterhead torque; (<b>c</b>) slurry pressure; (<b>d</b>) tunnelling speed.</p>
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<p>Transverse surface settlement troughs obtained from field measurements of the 3#~2# Shield Section of the Tsinghuayuan Tunnel.</p>
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<p>Horizontal displacement direction diagram of deep soil: (<b>a</b>) cross-section; (<b>b</b>) longitudinal section.</p>
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<p>Horizontal displacement of deep soil at different depths: (<b>a</b>) transverse direction; (<b>b</b>) longitudinal direction.</p>
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<p>Time history of horizontal displacement of deep soil in shield tunnelling: (<b>a</b>) transverse direction; (<b>b</b>) longitudinal direction.</p>
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<p>Three-dimensional numerical model.</p>
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<p>Comparison of horizontal settlement troughs.</p>
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<p>Comparison of surface monitoring point displacements over the tunnel axis during shield tunnelling.</p>
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<p>Horizontal displacement obtained from numerical simulation and field measurements: (<b>a</b>) transverse horizontal displacement; (<b>b</b>) longitudinal horizontal displacement.</p>
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<p>Longitudinal surface settlement based on the traditional isotropic Mohr–Coulomb model.</p>
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<p>Transverse surface settlement based on the cross-anisotropic model.</p>
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<p>Relationship between cross-anisotropic parameter α and maximum vertical displacement.</p>
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<p>Relationship between cross-anisotropic parameter α and transverse settlement trough width.</p>
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20 pages, 8034 KiB  
Article
Study on the Influence of Spatial Attributes on Passengers’ Path Selection at Fengtai High-Speed Railway Station Based on Eye Tracking
by Zhongzhong Zeng, Kun Zhang and Bo Zhang
Buildings 2024, 14(9), 3012; https://doi.org/10.3390/buildings14093012 - 22 Sep 2024
Abstract
The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of [...] Read more.
The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger’s wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger’s wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers’ The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers’ route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time > density of information > density of key information > diameter of the pupil. The study analyses the influencing factors affecting passengers’ wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations. Full article
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<p>Research framework.</p>
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<p>Pathfinding tasks segmentation. (The numbers 1 to 5 correspond to the five pathfinding nodes primarily analyzed in the experiment).</p>
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<p>Flowchart of pathfinding node.</p>
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<p>Percentage of each node’s information marking, i.e., marking density.</p>
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<p>Percentage of node-orientated destination signs, i.e., key information density at each node.</p>
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<p>Heat map of spatial connectivity for scenes ②–⑤ in depthmap software. Beta 1.0.</p>
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<p>Statistics of the correct rate of the subject’s wayfinding.</p>
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<p>Statistics of the correct rate of the subject’s wayfinding.</p>
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<p>Finding node AOI partitioning.</p>
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<p>Statistics of subjects’ gaze number in each scene.</p>
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<p>Mean and standard deviation of subjective ratings for each pathfinding node.</p>
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<p>Mean and standard deviation of subjective ratings for each pathfinding node.</p>
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<p>Logistic regression Exp(B) and its upper and lower limits for the evaluation index of the correctness rate of pathfinding.</p>
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26 pages, 15136 KiB  
Article
Experimental and Numerical Study on Lightweight-Foamed-Concrete-Filled Widened Embankment of High-Speed Railway
by Didi Hao, Changqing Miao, Shisheng Fang, Xudong Wang and Qiaoqiao Shu
Materials 2024, 17(18), 4642; https://doi.org/10.3390/ma17184642 - 21 Sep 2024
Abstract
To study the performance of lightweight foamed concrete (LWFC) in widened embankments of high-speed railways, this study first conducted numerous strength, permeability, and water immersion tests to investigate the mechanical properties and water resistance of LWFC with designed dry densities of 550, 600, [...] Read more.
To study the performance of lightweight foamed concrete (LWFC) in widened embankments of high-speed railways, this study first conducted numerous strength, permeability, and water immersion tests to investigate the mechanical properties and water resistance of LWFC with designed dry densities of 550, 600, and 650 kg/m3. Secondly, a field test was performed to analyze the behavior of the deformation and the internal pressure within the LWFC-filled portions. Furthermore, a parametric study via numerical modeling was performed to investigate the effects of four key factors on the performance of the LWFC-filled, widened embankments. Results showed that LWFC possesses adequate bearing capacity and impermeability to meet high-speed railway embankment widening requirements. However, water seepage reduces LWFC strength. The additional pressure from LWFC filling increases initially but then decreases once dehydration occurs. The settlement induced by LWFC accounted for 71% of the total filling height, which is only 37.5% of the total settlement after construction. The parametric study results show that the maximum settlement of widened and existing portions induced by LWFC was 46.3–49.6% and 48.3–53.2% of those induced by traditional fillers due to the LWFC’s lower density as well as their better self-supporting ability. Making an appropriate reduction in the thickness of the retain wall installed against the LWFC-filled widened embankment of the high-speed railway generates a few variations in the lateral deformation of the wall. Furthermore, the effects of the pile offset on the deformation of the LWFC-filled embankment were more sensitive compared to the diameter of the piles. Full article
(This article belongs to the Section Construction and Building Materials)
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<p>Distribution of deformation and internal forces in embankment of high-speed railway due to widening effects.</p>
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<p>Density and unconfined compressive strength of different geotechnical fillers.</p>
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<p>Preparing procedure of LWFC specimens.</p>
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<p>Compressive strength test.</p>
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<p>Tensile strength test. (<b>a</b>) Splitting test, (<b>b</b>) Bending test.</p>
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<p>Permeability test. (<b>a</b>) Sealing wax on the side of specimen, (<b>b</b>) Forcing the specimen into mold, (<b>c</b>) Installing the specimen into penetrator, (<b>d</b>) Parameter setting before test.</p>
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<p>Compressive strength test of LWFC treated by water. (<b>a</b>) Immersing specimens into water, (<b>b</b>) Weighing the specimen, (<b>c</b>) Testing the soaked specimen.</p>
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<p>Failure diagram of specimens after compressive and tensile tests. (<b>a</b>) Compressive failure, (<b>b</b>) Splitting failure, (<b>c</b>) Bending failure.</p>
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<p>Fitted curve of compressive strength versus dry density.</p>
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<p>Fitted curve of elastic modulus versus dry density.</p>
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<p>Fitted curve of splitting tensile strength versus dry density.</p>
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<p>Fitted curve of bending tensile strength versus dry density.</p>
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<p>Permeability test. (<b>a</b>) Local osmotic phenomenon, (<b>b</b>) Seepage velocity reaches stable.</p>
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<p>Fitted curve of permeability coefficient versus dry density.</p>
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<p>Specimens in the water immersion tests, (<b>a</b>) Cutting profile of the soaked specimens, (<b>b</b>) Compressed failure of the soaked specimens.</p>
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<p>Field-test profile of the widened embankment. (<b>a</b>) Composition of the widened embankment, (<b>b</b>) Measuring-point layout in the cross-section.</p>
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<p>Numerical modeling for the analysis.</p>
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<p>Strength criteria of different constitutive models, (<b>a</b>) Mohr–Coulomb failure criterion. (<b>b</b>) Mohr–Coulomb model used in the definition of the flow rule. (<b>c</b>) MCC failure criterion.</p>
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<p>Settlement data comparison between measurement and simulation, (<b>a</b>) Settlement data comparison between measurement and simulation (S-1), (<b>b</b>) Settlement data comparison between measurement and simulation (S-2).</p>
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<p>Pressure data comparison between measurement and simulation. (<b>a</b>) Monitoring points P-3,4,5. (<b>b</b>) Monitoring points P-6,7,8. (<b>c</b>) Monitoring points P-1,2.</p>
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<p>Horizontal deformation data comparison between measurement and simulation. (<b>a</b>) NSP. (<b>b</b>) FSP.</p>
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<p>Influence of the filler type on settlement of embankments, (<b>a</b>) The widened embankment, (<b>b</b>) The existing embankment.</p>
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<p>Influence of filler type on the horizontal deformation of the retaining wall.</p>
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<p>Influence of filler type on the horizontal deformation of piles. (<b>a</b>) NSP. (<b>b</b>) FSP.</p>
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<p>Influence of pile thickness on the horizontal deformation on the retaining wall.</p>
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<p>Influence of pile diameter and pile offset on settlement of foundation under the widened embankment.</p>
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<p>The maximum settlement of foundation under the widened embankment versus pile diameter and the offset.</p>
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<p>Influence of pile diameter and pile offset on the horizontal deformation of piles. (<b>a</b>) NSP. (<b>b</b>) FSP.</p>
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23 pages, 15414 KiB  
Article
Research on Vault Settlement during Three-Step Tunnel Construction Process Based on Sandstone Rheological Experiment
by Chang Peng, Yong Qu, Helin Fu, Chengda Xie and Guiqian Cao
Materials 2024, 17(18), 4619; https://doi.org/10.3390/ma17184619 - 20 Sep 2024
Abstract
Tunnel stability is influenced by the rheological properties of the surrounding rock. This study, based on the Ganshen high-speed railway tunnel project, examines the rheological characteristics of siltstone and sandstone through laboratory tests and theoretical analysis. Rheological curves and parameters are derived, revealing [...] Read more.
Tunnel stability is influenced by the rheological properties of the surrounding rock. This study, based on the Ganshen high-speed railway tunnel project, examines the rheological characteristics of siltstone and sandstone through laboratory tests and theoretical analysis. Rheological curves and parameters are derived, revealing the time-dependent deformation mechanisms of the surrounding rocks. A numerical simulation model is created using these parameters to analyze deformation and stress characteristics based on different rock levels and inverted arch closure distances. Results indicate that sandstone follows the Cvisc model, with the Maxwell elastic modulus increasing under higher loads while the viscous coefficient decreases. The vault displacement is mainly affected by the surrounding rock strength; lower strength leads to greater displacement, which also increases with the closure distance of the inverted arch. These findings are crucial for determining the optimal closure distance of inverted arches in sandstone conditions. Full article
(This article belongs to the Special Issue Advance in Sustainable Construction Materials, Second Volume)
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<p>Rock fully automatic triaxial compression servo.</p>
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<p>Failure Image.</p>
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<p>Rheological curves of grade III sandstone surrounding rocks under various loads.</p>
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<p>Rheological curves of grade III sandstone surrounding rocks under various loads.</p>
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<p>Rheological curves of grade IV sandstone surrounding rocks under various loads.</p>
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<p>Rheological curves of grade V sandstone surrounding rocks under various loads.</p>
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<p>Cvisc rheological model.</p>
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<p>Surrounding rock test and Cvisc rheological model.</p>
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<p>Surrounding rock test and Cvisc rheological model.</p>
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<p>Surrounding rock test and Cvisc rheological model.</p>
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<p>Maxwell model parameter change diagram.</p>
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<p>Maxwell model parameter change diagram.</p>
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<p>Kelvin model parameter change diagram.</p>
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<p>Numerical analysis and calculation model.</p>
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<p>Comparison of simulated values and monitored values.</p>
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<p>Diagram of three-step excavation.</p>
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<p>Typical burgers creep model and characteristic curve. (<b>a</b>) creep model; (<b>b</b>) creep curve.</p>
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<p>Deformation nephogram of three-step excavation tunnel with modified Burgers creep model.</p>
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<p>Deformation nephogram of three-step excavation tunnel with Mohr–Coulomb criterion.</p>
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<p>Settlement curve with Mohr–Coulomb model.</p>
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<p>Variation curve of vault settlement.</p>
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<p>Tunnel vault settlement curves caused by different levels of surrounding rock and different excavation methods.</p>
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<p>Results comparison of the model proposed in this paper and the M-C model.</p>
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16 pages, 16974 KiB  
Article
Effect of Sintering Temperature on the Microstructure and Mechanical and Tribological Properties of Copper Matrix Composite for Brake Pads
by Yajun Zhou, Yongzhen Zhang, Xin Zhang, Jianxiu Liu and Mingxin Wang
Metals 2024, 14(9), 1048; https://doi.org/10.3390/met14091048 - 14 Sep 2024
Abstract
Copper-based powder metallurgy materials are frequently utilized in fabricating brake pads for high-speed trains. The preparation process involves mixing, ball milling, pressing, and sintering. Among these steps, hot-pressed sintering stands out as a rapid and efficient method that significantly influences the properties and [...] Read more.
Copper-based powder metallurgy materials are frequently utilized in fabricating brake pads for high-speed trains. The preparation process involves mixing, ball milling, pressing, and sintering. Among these steps, hot-pressed sintering stands out as a rapid and efficient method that significantly influences the properties and performance of the products. In this study, four samples (S700/S750/S800/S850) were prepared using hot-pressed sintering at various temperatures, as follows: 700 °C, 750 °C, 800 °C, and 850 °C. The mechanical and physical properties of the four samples were tested, and the microstructure and compositions were investigated using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction. The findings highlighted the close relationship between sintering temperature and the mechanical and physical properties of the samples, as it impacts the porosity and interfacial bonding of the particles. Notably, Sample S800 demonstrated superior mechanical and thermal conductivity. Furthermore, the coefficient of friction (COF), friction heat, and wear rate of the four samples were also tested under different braking speeds ranging from 150 km/h to 350 km/h. The results indicated that the COFs of the four samples remained relatively stable below 300 km/h but decreased notably above 300 km/h due to heat fading. Sample S800 displayed consistent and high COF under varied braking speeds and exhibited the lowest wear rate. The observed wear mechanisms included abrasive wear and oxidation wear. Additionally, the friction test results underscored the close correspondence of the COF curve of S800 with the standard of the Ministry of Railways of the People’s Republic of China. Full article
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<p>Four sintering process curves.</p>
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<p>Scheme of the MM3000 friction wear testing machine.</p>
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<p>X-ray analyses of samples S700~S850.</p>
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<p>Crystallite size of the four samples.</p>
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<p>SEM images of the four samples: (<b>a</b>) S700, (<b>b</b>) S750, (<b>c</b>) S800, and (<b>d</b>) S850. (<b>e</b>) Close-up of zone 1 in S750; (<b>f</b>) close-up of zone 2 in S800.</p>
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<p>Physical and mechanical properties of four samples.</p>
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<p>Results of friction and wear tests conducted under different initial braking speeds. Shown are data corresponding to (<b>a</b>) maximum subsurface temperature, (<b>b</b>) average COF curves, (<b>c</b>) line wear rate, and (<b>d</b>) COF of sample S950 compared with industrial standard.</p>
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<p>The three-dimensional morphologies of the four samples after the braking test. Shown are the results for (<b>a</b>) sample S700, (<b>b</b>) sample S750, (<b>c</b>) sample S800, and (<b>d</b>) sample S850.</p>
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<p>Surface morphology (<b>a</b>–<b>d</b>) and EDS line scanning (<b>e</b>,<b>f</b>) of the four samples after the braking test. Shown are the results for (<b>a</b>,<b>e</b>) sample S700, (<b>b</b>,<b>f</b>) sample S 750, (<b>c</b>,<b>g</b>) sample S800, and (<b>d</b>,<b>h</b>) sample S850.</p>
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<p>SEM image and EDS spectra of S800. Shown are data corresponding to (<b>a</b>) S800, specifically, (<b>b</b>) spot 1, (<b>c</b>) spot 2, (<b>d</b>) spot 3, (<b>e</b>) spot 4, (<b>f</b>) spot 5, (<b>g</b>) spot 6, and (<b>h</b>) line 1.</p>
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<p>Chipping pit morphologies of the four samples after the braking test. Shown are the results for (<b>a</b>) sample S700, (<b>b</b>) sample S750, (<b>c</b>) sample S800, and (<b>d</b>) sample S850.</p>
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22 pages, 10522 KiB  
Article
Application of PS-InSAR and Diagnostic Train Measurement Techniques for Monitoring Subsidence in High-Speed Railway in Konya, Türkiye
by Gokhan Kizilirmak and Ziyadin Cakir
Infrastructures 2024, 9(9), 152; https://doi.org/10.3390/infrastructures9090152 - 7 Sep 2024
Abstract
Large-scale man-made linear structures like high-speed railway lines have become increasingly important in modern life as a faster and more comfortable transportation option. Subsidence or longitudinal levelling deformation problems along these railway lines can prevent the line from operating effectively and, in some [...] Read more.
Large-scale man-made linear structures like high-speed railway lines have become increasingly important in modern life as a faster and more comfortable transportation option. Subsidence or longitudinal levelling deformation problems along these railway lines can prevent the line from operating effectively and, in some cases, require speed reduction, continuous maintenance or repairs. In this study, the longitudinal levelling deformation of the high-speed railway line passing through Konya province (Central Turkey) was analyzed for the first time using the Persistent Scatter Synthetic Aperture Radar Interferometry (PS-InSAR) technique in conjunction with diagnostic train measurements, and the correlation values between them were found. In order to monitor potential levelling deformation along the railway line, medium-resolution, free-of-charge C-band Sentinel-1 (S-1) data and high-resolution, but paid, X-band Cosmo-SkyMed (CSK) Synthetic Aperture Radar (SAR) data were analyzed from the diagnostic train and reports received from the relevant maintenance department. Comparison analyses of the results obtained from the diagnostic train and radar measurements were carried out for three regions with different deformation scenarios, selected from a 30 km railway line within the whole analysis area. PS-InSAR measurements indicated subsidence events of up to 40 mm/year along the railway through the alluvial sediments of the Konya basin, which showed good agreement with the diagnostic train. This indicates that the levelling deformation of the railway and its surroundings can be monitored efficiently, rapidly and cost-effectively using the InSAR technique. Full article
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<p>Photograph depicting a section of the Ankara–Konya High-Speed Railways provided by the Gokhan Kizilirmak.</p>
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<p>Geological maps: (<b>a</b>) shows the 1st study area; (<b>b</b>) shows the 2nd study area.</p>
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<p>Roger-800 performing measurements on the Ankara–Konya high-speed railway. The image was provided by Gokhan Kizilirmak.</p>
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<p>Photo showing the position of the laser measurement sensors. The image is provided by the Gokhan Kizilirmak.</p>
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<p>Illustration showing the working principle of levelling on a diagnostic train.</p>
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<p>Displacement diagram of the railway in the line of sight and at multiple passes.</p>
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<p>Simplified workflow of PS-InSAR processing in SARPROZ© (adapted from [<a href="#B58-infrastructures-09-00152" class="html-bibr">58</a>,<a href="#B59-infrastructures-09-00152" class="html-bibr">59</a>]).</p>
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<p>Image graphs for each time-series data stack: (<b>a</b>) CSK ascending; (<b>b</b>) S1–B/T65 descending; and (<b>c</b>) S1–B /T160 ascending. They show the 2D spatiotemporal baseline (yyyymmdd) spaces. Each point displays a scene, and each line displays an interferogram concerning a single master, which is represented with a red color dot.</p>
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<p>Reflectivity map showing the reference point location, city center and railway with blue colored text from all radar images.</p>
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<p>PSC maps and scatter plots: (<b>a</b>) CSK; (<b>b</b>) S1–B/T65; (<b>c</b>) S1–B/T160. PSC maps (red line means the railway) and mean velocity maps for CSK and S-1 analyses in LOS direction (dark blue line represents the 30 km-long railway).</p>
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<p>Vertical accumulated subsidence profiles of the railway along the 1st and 2nd study areas: (<b>a</b>) CSK; (<b>b</b>) S1–B/T65; (<b>c</b>) S1–B/T160; and (<b>d</b>) diagnostic train measurement time-series graphs.</p>
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<p>Accumulated subsidence graphs for the clustered PSs, where blue color represents PSs from CSK, orange color signs PSs from S1–B /T160, and lastly, grey color denotes PSs from S1–B /T65: (<b>a</b>) Location#1; (<b>b</b>) Location#2; (<b>c</b>) Location#3; and (<b>d</b>) Location#4.</p>
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<p>Specialized workflow model.</p>
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<p>Map of Ankara–Konya High-Speed railway showing the study areas.</p>
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18 pages, 4881 KiB  
Article
An Improved Deep Deterministic Policy Gradient Pantograph Active Control Strategy for High-Speed Railways
by Ying Wang, Yuting Wang, Xiaoqiang Chen, Yixuan Wang and Zhanning Chang
Electronics 2024, 13(17), 3545; https://doi.org/10.3390/electronics13173545 - 6 Sep 2024
Abstract
The pantograph–catenary system (PCS) is essential for trains to obtain electrical energy. As the train’s operating speed increases, the vibration between the pantograph and the catenary intensifies, reducing the quality of the current collection. Active control may significantly reduce the vibration of the [...] Read more.
The pantograph–catenary system (PCS) is essential for trains to obtain electrical energy. As the train’s operating speed increases, the vibration between the pantograph and the catenary intensifies, reducing the quality of the current collection. Active control may significantly reduce the vibration of the PCS, effectively lower the cost of line retrofitting, and enhance the quality of the current collection. This article proposes an improved deep deterministic policy gradient (IDDPG) for the pantograph active control problem, which delays updating the Actor and Target–Actor networks and adopts a reconstructed experience replay mechanism. The deep reinforcement learning (DRL) environment module was first established by creating a PCS coupling model. On this basis, the controller’s DRL module is precisely designed using the IDDPG strategy. Ultimately, the control strategy is integrated with the PCS for training, and the controller’s performance is validated on the PCS. Simulation experiments show that the improved strategy significantly reduces the training time, enhances the steady-state performance of the agent during later training stages, and effectively reduces the standard deviation of the pantograph–catenary contact force (PCCF) by an average of over 51.44%, effectively improving the quality of current collection. Full article
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<p>Schematic diagram of the high-speed railway PCS.</p>
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<p>DRL-based active control diagram for pantographs.</p>
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<p>Pantograph lumped mass model.</p>
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<p>Definition of Markov decision-making process.</p>
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<p>Improved experience replay buffer <span class="html-italic">D</span><sub>2</sub>.</p>
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<p>Network structure. (<b>a</b>) Actor network structure. (<b>b)</b> Critic network structure.</p>
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<p>Flowchart of IDDPG strategy.</p>
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<p>DDPG vs. IDDPG algorithm reward function curves: (<b>a</b>) DDPG; (<b>b</b>) IDDPG.</p>
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<p>IDDPG algorithm network loss function: (<b>a</b>) Actor network; (<b>b</b>) Critic network.</p>
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<p>PCCF and active control force comparison at 360 km/h: (<b>a</b>) PCCF; (<b>b</b>) active control force.</p>
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<p>PCCF and STD comparison for various control strategies at various speeds: (<b>a</b>) comparison of PCCF at 280 km/h; (<b>b</b>) comparison of PCCF at 320 km/h; (<b>c</b>) comparison of PCCF at 360 km/h; and (<b>d</b>) comparison of STD at different speeds.</p>
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<p>Comparison of PCCF and STD under China Beijing–Shanghai Railway line: (<b>a</b>) comparison of PCCF at 360 km/h; (<b>b</b>) comparison of STD at different speeds.</p>
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20 pages, 5402 KiB  
Article
Research on Train-Induced Vibration of High-Speed Railway Station with Different Structural Forms
by Xiangrong Guo, Jianghao Liu and Ruibo Cui
Materials 2024, 17(17), 4387; https://doi.org/10.3390/ma17174387 - 5 Sep 2024
Abstract
Elevated stations are integral components of urban rail transit systems, significantly impacting passengers’ travel experience and the operational efficiency of the transportation system. However, current elevated station designs often do not sufficiently consider the structural dynamic response under various operating conditions. This oversight [...] Read more.
Elevated stations are integral components of urban rail transit systems, significantly impacting passengers’ travel experience and the operational efficiency of the transportation system. However, current elevated station designs often do not sufficiently consider the structural dynamic response under various operating conditions. This oversight can limit the operational efficiency of the stations and pose potential safety hazards. Addressing this issue, this study establishes a vehicle-bridge-station spatial coupling vibration simulation model utilizing the self-developed software GSAP V1.0, focusing on integrated station-bridge and combined station-bridge elevated station designs. The simulation results are meticulously compared with field data to ensure the fidelity of the model. Analyzing the dynamic response of the station in relation to train parameters reveals significant insights. Notably, under similar travel conditions, integrated stations exhibit lower vertical acceleration in the rail-bearing layer compared to combined stations, while the vertical acceleration patterns at the platform and hall layers demonstrate contrasting behaviors. At lower speeds, the vertical acceleration at the station concourse level is comparable for both station types, yet integrated stations exhibit notably higher platform-level acceleration. Conversely, under high-speed conditions, integrated stations show increased vertical acceleration at the platform and hall levels compared to combined stations, particularly under unloaded double-line working conditions, indicating a superior dynamic performance of combined stations in complex operational scenarios. However, challenges such as increased station height due to bridge box girder maintenance, track layer waterproofing, and track girder support maintenance exist for combined stations, warranting comprehensive evaluation for station selection. Further analysis of integrated station-bridge structures reveals that adjustments in the floor slab thickness at the rail-bearing and platform levels significantly reduce dynamic responses, whereas increasing the rail beam height notably diminishes displacement responses. Conversely, alterations in the waiting hall floor slab thickness and frame column cross-sections exhibit a minimal impact on the station dynamics. Overall, optimizing structural dimensions can effectively mitigate dynamic responses, offering valuable insights for station design and operation. Full article
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<p>Schematic diagram of station-bridge structure. (<b>a</b>) Schematic elevation of station-bridge structure (hiding roof structure); (<b>b</b>) section of integrated station-bridge structure; (<b>c</b>) section of combined station-bridge structure.</p>
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<p>The topology model of the high-speed train: (<b>a</b>) elevation view; (<b>b</b>) side view; (<b>c</b>) plan view; (<b>d</b>) schematic diagram of the vehicle’s horizontal spring.</p>
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<p>Finite element model of integrated station-bridge system: (<b>a</b>) axonometric view; (<b>b</b>) front elevation.</p>
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<p>Finite element model of separated station-bridge system: (<b>a</b>) axonometric view; (<b>b</b>) front elevation.</p>
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<p>First-order vibration pattern of integrated station-bridge system. (<b>a</b>) first mode; (<b>b</b>) second Mode; (<b>c</b>) eleventh Mode.</p>
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<p>First-order vibration pattern of separated station-bridge system. (<b>a</b>) First mode; (<b>b</b>) second mode; (<b>c</b>) eleventh mode.</p>
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<p>Vertical displacement of station-bridge structures: (<b>a</b>) integrated station-bridge structure; (<b>b</b>) combined station-bridge structure.</p>
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<p>Field test photos of the integrated station-bridge structure.</p>
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<p>The field test data and theoretical calculation data of integrated station-bridge structure. (<b>a</b>) measured acceleration; (<b>b</b>) calculated acceleration.</p>
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<p>Maximum vertical acceleration of the station under single-line condition. (<b>a</b>) Rail-bearing layer; (<b>b</b>) hall layer; (<b>c</b>) platform level.</p>
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<p>Maximum vertical acceleration of the station under double-line condition. (<b>a</b>) Rail-bearing layer; (<b>b</b>) hall layer; (<b>c</b>) platform level.</p>
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<p>Relationship curve between station dynamic response and track girder height. (<b>a</b>) Displacement; (<b>b</b>) acceleration.</p>
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<p>Relationship curve between station dynamic response and thickness of rail-bearing layer. (<b>a</b>) Displacement; (<b>b</b>) acceleration.</p>
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<p>Relationship curve between station dynamic response and thickness of hall layer. (<b>a</b>) Displacement; (<b>b</b>) acceleration.</p>
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<p>Relationship curve between station dynamic response and thickness of platform level. (<b>a</b>) Displacement; (<b>b</b>) acceleration.</p>
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<p>Relationship curve between station dynamic response and section size of hall layer columns. (<b>a</b>) Displacement; (<b>b</b>) acceleration.</p>
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18 pages, 7541 KiB  
Article
A Backpropagation-Based Algorithm to Optimize Trip Assignment Probability for Long-Term High-Speed Railway Demand Forecasting in Korea
by Ho-Chan Kwak
Appl. Sci. 2024, 14(17), 7880; https://doi.org/10.3390/app14177880 - 4 Sep 2024
Viewed by 156
Abstract
In Korea, decisions for high-speed railway (HSR) construction are made based on long-term demand forecasting. A calibration process that simulates current trip patterns is an important step in long-term demand forecasting. However, a trial-and-error approach based on iterative parameter adjustment is used for [...] Read more.
In Korea, decisions for high-speed railway (HSR) construction are made based on long-term demand forecasting. A calibration process that simulates current trip patterns is an important step in long-term demand forecasting. However, a trial-and-error approach based on iterative parameter adjustment is used for calibration, resulting in time inefficiency. In addition, the all-or-nothing-based optimal strategy algorithm (OSA) used in HSR trip assignment has limited accuracy because it assigns all trips from a zone with multiple accessible stations to only one station. Therefore, this study aimed to develop a backpropagation-based algorithm to optimize trip assignment probability from a zone to multiple accessible HSR stations. In this algorithm, the difference between the estimated volume calculated from the trip assignment probability and observed volumes was defined as loss, and the trip assignment probability was optimized by repeatedly updating in the direction of the reduced loss. The error rate of the backpropagation-based algorithm was compared with that of the OSA using KTDB data; the backpropagation-based algorithm had lower errors than the OSA for most major HSR stations. It was especially superior when applied to areas with multiple HSR stations, such as the Seoul metropolitan area. This algorithm will improve the accuracy and time efficiency of long-term HSR demand forecasting. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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<p>The HSR network in Korea [<a href="#B2-applsci-14-07880" class="html-bibr">2</a>].</p>
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<p>Standard long-term HSR demand-forecasting procedure in Korea.</p>
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<p>The trip assignment probability concept.</p>
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<p>The backpropagation-based optimization algorithm in this study.</p>
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<p>Calculation of probability weight tensor (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>).</p>
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<p>Calculation converting the probability weight to the probability.</p>
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<p>Calculation used to assign the total trip values for each station using their respective probabilities.</p>
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<p>Calculation of the boarding/alighting volume by stations.</p>
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<p>HSR network data in the KTDB.</p>
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<p>The loss changes in the backpropagation-based algorithm by iteration.</p>
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<p>The code for variable initialization.</p>
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<p>The code for optimizing the trip assignment probability weight.</p>
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<p>The code for saving the optimized results after the last updating process.</p>
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16 pages, 2442 KiB  
Article
Low Stress Level and Low Stress Amplitude Fatigue Loading Simulation of Concrete Components Containing Cold Joints under Fatigue Loading
by He-Lin Fu, Huang-Shi Deng, Yi-Min Wu, Yi-Bo Zhao and Cheng-Da Xie
Appl. Sci. 2024, 14(17), 7709; https://doi.org/10.3390/app14177709 - 31 Aug 2024
Viewed by 424
Abstract
Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage [...] Read more.
Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage of concrete linings containing cold joints. Based on the Roe-Siegmund cycle cohesive force model, a cohesive force fatigue damage elements were developed. A large dataset was constructed through numerical simulation software to build a BP neural network for back-calculated parameter of cohesive force fatigue damage elements. By combining experimental data, fatigue damage parameters corresponding to different pouring interval cold joints were back-calculated. These back-calculated parameters were then incorporated into the numerical model to compare simulation results with experimental results to validate the applicability of cohesive force fatigue damage elements and back propagation neural networks (BP neural network). The research results show that the difference between the fatigue life and fracture process calculated by numerical simulation and experimental data is small, verifying the applicability of the method proposed in this paper. The pouring interval directly affects the initial strength of the cold joint interface and the starting conditions of fatigue damage. The possibility of fatigue damage and fracture of concrete components containing cold joints increases with the increase of pouring interval, while the variability of fatigue life decreases with the increase of pouring interval. Interface strength and thickness are the main factors affecting the possibility of fatigue damage occurrence and the variability of fatigue life. The research results can be used to analyze the damage and cracking status of concrete linings containing cold joints under aerodynamic fatigue loading. Full article
(This article belongs to the Special Issue Advances in Sustainable Geotechnical Engineering: 2nd Edition)
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<p>Cohesive zone model (CZM).</p>
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<p>Bilinear traction separation law.</p>
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<p>Bilinear traction separation law under cyclic loading.</p>
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<p>State of cohesive force unit under cyclic load.</p>
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<p>Compilation ideas for cohesive fatigue damage model.</p>
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<p>Numerical Calculation Model.</p>
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<p>BP neural network.</p>
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<p>Neural Network Training Results.</p>
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<p>Changes in fatigue life with cyclic strength. (<b>a</b>) 2 h. (<b>b</b>) 4 h. (<b>c</b>) 8 h.</p>
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<p>Reasons for discrete fatigue life of different pouring intervals.</p>
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<p>Fatigue cracking process and cold joint damage status.</p>
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18 pages, 13370 KiB  
Article
Evaluation Method of the Impact of Twin Shield Tunneling Construction on Elevated Bridges: Case Study
by Junzhou Huang, Jizhixian Liu, Kai Guo, Shan Yang, Yani Lu, Ying Wang and Cai Wu
Symmetry 2024, 16(9), 1113; https://doi.org/10.3390/sym16091113 - 27 Aug 2024
Viewed by 319
Abstract
In urban metro construction, shield tunneling often needs to pass through building and bridge pile foundations, potentially affecting the stability of existing structures. Therefore, accurately assessing the impact of shield tunneling on bridges and buildings is crucial. This study presents a comprehensive prediction [...] Read more.
In urban metro construction, shield tunneling often needs to pass through building and bridge pile foundations, potentially affecting the stability of existing structures. Therefore, accurately assessing the impact of shield tunneling on bridges and buildings is crucial. This study presents a comprehensive prediction method combining numerical simulation and empirical formulas, taking the underpass project of the Shijiazhuang–Wuhan High-Speed Railway Bridge by Zhengzhou Metro Line 5 as a case study. Three-dimensional numerical model calculations were performed using finite element software to analyze the displacement and stress changes of buildings and tunnel structures at different construction stages, revealing the deformation patterns of buildings adjacent to the tunnel during shield tunneling. In particular, the ground settlement caused by twin-tunnel excavation was compared with Peck’s empirical formula to verify the reliability of the numerical simulation. The results show that twin-tunnel excavation exacerbates the horizontal displacement, uplift, and settlement of the ground, with maximum deformation rates increasing by 7.10%, 20%, and 11.4%, respectively. Comparing the ground deformation results of Peck’s empirical formulas with numerical calculations revealed similar trends in the settlement curves, with a maximum deviation of 6.67%. It can be concluded that using Peck’s empirical formula to calculate ground deformation characteristics complements the limitations of numerical simulations, making the assessment results more reliable. The findings of this study demonstrate that integrating numerical simulation with empirical formulas significantly enhances the reliability of deformation predictions in complex tunneling scenarios. This research not only offers a comprehensive safety assessment method for shield tunneling construction but also provides valuable guidance for the design and construction of similar projects, serving as a theoretical reference for future engineering endeavors. Full article
(This article belongs to the Section Engineering and Materials)
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<p>Cross-section of Zhengzhou Metro Line 5 crossing under four connecting bridges from CK18+060.600 to CK19+834.800.</p>
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<p>Plan view of the section tunnel and connection bridges.</p>
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<p>Spatial relationship between the section tunnel and the SW1 Bridge.</p>
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<p>Three-dimensional model of the section tunnel and SW1 Bridge: (<b>a</b>) overview; (<b>b</b>) the position of the shield tunnel and the bridge piles.</p>
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<p>Schematic diagram of the eight typical construction steps for twin-bore shield tunnel excavation: (<b>a</b>) S1; (<b>b</b>) S2; (<b>c</b>) S3; (<b>d</b>) S4; (<b>e</b>) S5; (<b>f</b>) S6; (<b>g</b>) S7; (<b>h</b>) S8.</p>
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<p>Schematic diagram of the eight typical construction steps for twin-bore shield tunnel excavation: (<b>a</b>) S1; (<b>b</b>) S2; (<b>c</b>) S3; (<b>d</b>) S4; (<b>e</b>) S5; (<b>f</b>) S6; (<b>g</b>) S7; (<b>h</b>) S8.</p>
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<p>Horizontal displacement contour map of ground during full excavation of shield tunnel: (<b>a</b>) S1; (<b>b</b>) S2; (<b>c</b>) S3; (<b>d</b>) S4; (<b>e</b>) S5; (<b>f</b>) S6; (<b>g</b>) S7; (<b>h</b>) S8.</p>
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<p>Vertical displacement contour map of ground during full excavation of shield tunnel (<b>a</b>) S1; (<b>b</b>) S2; (<b>c</b>) S3; (<b>d</b>) S4; (<b>e</b>) S5; (<b>f</b>) S6; (<b>g</b>) S7; (<b>h</b>) S8.</p>
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<p>Vertical displacement contour map of ground during full excavation of shield tunnel (<b>a</b>) S1; (<b>b</b>) S2; (<b>c</b>) S3; (<b>d</b>) S4; (<b>e</b>) S5; (<b>f</b>) S6; (<b>g</b>) S7; (<b>h</b>) S8.</p>
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<p>The deformation of pile caps during the eight excavation stages of the section shield tunnel: (<b>a</b>) maximum horizontal deformation curves; (<b>b</b>) maximum vertical settlement curves.</p>
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<p>Cloud diagrams for pile foundations in Model A after the tunnel excavation: (<b>a</b>) horizontal displacement at S4; (<b>b</b>) settlement displacements at S4; (<b>c</b>) horizontal displacement at S8; (<b>d</b>) settlement displacements at S8.</p>
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<p>Displacement cloud diagrams of horizontal and vertical deformations of the bridge deck and piers as the shield tunnel excavation crosses the Southwest Upward Connection Line Bridge.</p>
Full article ">Figure 11
<p>Transverse surface settlement curve.</p>
Full article ">Figure 12
<p>Comparison between the numerical and theoretical results of ground surface deformation: (<b>a</b>) single-line shield tunnel excavation (S4); (<b>b</b>) double-line shield tunnel excavation (S8).</p>
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