Irregular Characteristics Research Articles

A Planar Feature-Preserving Texture Defragmentation Method for 3D Urban Building Models

Oblique photogrammetry-based 3D modeling is widely used for large-scale urban reconstruction. However, textures generated with photogrammetric techniques often exhibit scattered and irregular characteristics, which lead to significant challenges with texture seams and UV map discontinuities, increasing storage requirements and affecting rendering quality. In this paper, we propose a planar feature-preserving texture defragmentation method designed specifically for urban building models. Our approach leverages the multi-planar topology of buildings to optimize texture merging and reduce fragmentation. The proposed approach is composed of three main stages: the extraction of planar features from texture fragments; the employment of these planar elements as guiding constraints for merging adjacent texture fragments within a two-dimensional texture space; and an enhanced texture-packing algorithm designed for more regular texture charts to systematically generate refined texture atlas. Experiments on various urban building models demonstrate that our method significantly improves texture continuity and storage efficiency compared to traditional approaches, with both quantitative and qualitative validation.

Research on Spatial Morphological Characteristics and Influencing Factors of Industrial Heritage: A Case Study of Nine Industrial Heritages in Guizhou Province

Industrial heritage, recognized as a significant aspect of historical and cultural heritage, has garnered considerable attention from scholars globally. To elucidate the spatial morphological characteristics and the underlying influencing factors of industrial heritage within karst regions, this study employs methods such as the interstice index, fractal dimension analysis, and spatial syntax. It conducts research on the spatial morphological characteristics of nine typical industrial heritages in Guizhou Province. The primary factors contributing to the variations in layout forms are the intricate karst topography and the functional requirements of production. The functional zoning of industrial heritage aligns with its layout, characterized by straightforward functional zones that have not developed into composite spaces. The overall connectivity of industrial heritage is relatively low, exhibiting weak integration, significant disparities in control values, low average depth values, and a deficiency in comprehensibility and diversity of options. This indicates that the internal connectivity of industrial heritage spaces is generally inadequate, with low accessibility, strong interrelations, average convenience, limited connectivity, and generally acceptable passage. The overall spatial, architectural, and roadway configurations of industrial heritage predominantly exhibit a uniform pattern. Importantly, industrial heritage reveals a highly variable overall spatial form, with an average fractal dimension of 1.57, complex architectural layouts (average fractal dimension of 1.50), and simplistic road network designs (average fractal dimension of 1.43), which collectively suggest high spatial complexity and irregular characteristics. This study can provide a reference for the analysis of spatial characteristics and influencing factors of other material cultural heritages, and it is of great significance for the systematic protection and revitalization of industrial heritage.

Dual‐channel observer design for composite disturbances based on characteristic compensation

AbstractThis paper presents a non‐coupled structure of characteristic compensation dual‐channel observer (CCDCO) for handling systems subjected to composite disturbances. There exist an outer disturbance compensation channel (ODCC) and an inner disturbance compensation channel (IDCC) in the design strategy. Specifically, in the ODCC, the proposed frequency characteristic compensation observer (FCCO) extracts and removes the regular periodic components from the composite disturbances, where the estimation error preserves irregular characteristics. Thus, the estimation error of FCCO and irregular parts are treated as lumped terms estimated via compensation function observer (CFO) in the IDCC. As a solving skill for composite disturbances estimation, CCDCO has two important advantages over previous designs. First, the proposed non‐coupled structure explicitly distinguishes disturbance characteristics according to a priori information known or not. Second, by combining the benefits of the CFO and proposed FCCO, different types of disturbances can be estimated accordingly without coupling of each other. Simulation results on the robotic manipulator are provided to validate the effectiveness of the proposed method.

Evolution Characteristics of Aluminum Thermal Weld Irregularity and Damage in Heavy-Haul Railway under Different Service Conditions

Aluminum thermal welding joints are widely used in the maintenance welding of heavy-haul railways due to their easy handling and high efficiency. However, due to their inherent welding characteristics, welding results in certain differences in the material’s physical properties at the welding zone compared to adjacent base materials, leading to the occurrence of short-wave irregularity under long-term wheel–rail interactive forces. In order to explore the evolution characteristics of weld irregularity, dynamic characteristics, and plastic deformation under long-term wheel–rail impact, a detailed tracking test was conducted on a normal aluminum weld, and the process from being put on the track to being damaged and replaced was evaluated. At the same time, a rigid–flexible coupling model was established for subsequent analysis, and plastic damage was analyzed using the finite element model. The results show that the service life of the weld can be divided into three different stages: the initial stage, the intermediate stage, and the damage stage. In the damage stage, a temporary separation occurred between the wheel and rail, leading to a sudden change in the wheel–rail interaction. The weight of 250 MT at the weld reached the repairment control limit. The concentration effect of equivalent plastic deformation was most serious at 2~5 mm below the rail head.

Topographic variation and fluid flow characteristics in rough contact interface

Understanding flow characteristics of fluid near rough contact is important for the design of fluid-based lubrication and basic of tribology physics. In this study, the spreading and seepage processes of anhydrous ethanol in the interface between glass and rough PDMS are observed by a homemade optical in-situ tester. Digital image processing technology and numerical simulation software are adapted to identify and extract the topological properties of interface and thin fluid flow characteristics. Particular attention is paid to the dynamic evolution of the contact interface morphology under different stresses, the distribution of microchannels in the interface, the spreading characteristics of the fluid in contact interface, as well as the mechanical driving mechanism. Original surface morphology and the contact stress have a significant impact on the interface topography and the distribution of interfacial microchannels, which shows that the feature lengths of the microchannels, the spreading area and the spreading rate of the fluid are inversely proportional to the load. And the flow path of the fluid in the interface is mainly divided into three stages: along the wall of the island, generating liquid bridges, and moving from the tip side to the root side in the wedge-shaped channel. The main mechanical mechanism of liquid flow in the interface is the equilibrium between the capillary force that drives the liquid spreading and viscous resistance of solid wall to liquid. In addition, the phenomenon of “trapped air” occurs during the flow process due to the irregular characteristics of the microchannel. This study lays a certain theoretical foundation for the research of microscopic flow behavior of the liquid in the rough contact interface, the friction and lubrication of the mechanical system, and the sealing mechanism.

Directional HVSR at the Chalco lakebed zone of the Valley of Mexico: Analysis and interpretation

This study delves into Horizontal-to-Vertical Spectral Ratio (HVSR) and directional HVSR (D-HVSR) using continuous 30-min ambient seismic noise (ASN) signals recorded at 68 sites in the Chalco lakebed zone of the Valley of Mexico (VM). Our aim is to explore the shallow stratigraphy and subsurface structures in that zone. The isoperiod map using the HVSR, and the ensuing velocity structure inversion reveals a basin of irregular shape and shallow depth in the surroundings. Additionally, we explore azimuthal effects by computing the D-HVSR by rotating the records to obtain the time series for a given direction. We found different polarizations at the measurement points, and focused on the frequencies in which the effect is more pronounced. The cases with significant polarizations at the dominant frequencies suggest an irregularity at the basement level. This is supported by preliminary synthetic results that uses the three-dimensional (3D) Indirect Boundary Element Method (IBEM) to assess the effects of interface irregularities like a channel or dike. In the present stage of development, the D-HVSR is a powerful tool to reveal the existence of lateral variations. The complexity of the lakebed zone response in the VM and its irregular characteristics show the potential of the D-HVSR approach as well as the need to deeply explore into the modeling for its better understanding and interpretation.

The Characteristics of Long-Wave Irregularities in High-Speed Railway Vertical Curves and Method for Mitigation.

Track geometry measurements (TGMs) are a critical methodology for assessing the quality of track regularities and, thus, are essential for ensuring the safety and comfort of high-speed railway (HSR) operations. TGMs also serve as foundational datasets for engineering departments to devise daily maintenance and repair strategies. During routine maintenance, S-shaped long-wave irregularities (SLIs) were found to be present in the vertical direction from track geometry cars (TGCs) at the beginning and end of a vertical curve (VC). In this paper, we conduct a comprehensive analysis and comparison of the characteristics of these SLIs and design a long-wave filter for simulating inertial measurement systems (IMSs). This simulation experiment conclusively demonstrates that SLIs are not attributed to track geometric deformation from the design reference. Instead, imperfections in the longitudinal profile's design are what cause abrupt changes in the vehicle's acceleration, resulting in the measurement output of SLIs. Expanding upon this foundation, an additional investigation concerning the quantitative relationship between SLIs and longitudinal profiles is pursued. Finally, a method that involves the addition of a third-degree parabolic transition curve (TDPTC) or a full-wave sinusoidal transition curve (FSTC) is proposed for a smooth transition between the slope and the circular curve, designed to eliminate the abrupt changes in vertical acceleration and to mitigate SLIs. The correctness and effectiveness of this method are validated through filtering simulation experiments. These experiments indicate that the proposed method not only eliminates abrupt changes in vertical acceleration, but also significantly mitigates SLIs.

On the Gravity Wave‐Seeded Ionospheric Irregularities in the Martian Ionosphere

AbstractFor the past few decades, it has been demonstrated that gravity waves (GWs) and neutral winds can drive ionospheric irregularities on Earth. Still, as far as we know, the formation of ionospheric irregularities on Mars due to GWs has not been well studied. In this study, we use data from the NASA's Mars Atmosphere and Volatile Evolution (MAVEN) mission to show evidence of an irregularity event in the Martian ionosphere, potentially seeded by the GWs break (GWB). Statistical findings indicate that the observed ratio of GWB‐related irregularity events varies from ∼0.25 to ∼0.47 each year, and the average ratio in 2015–2020 is ∼0.37. We perform a numerical simulation to provide further insight into the processes behind irregularity formation, which employs neutral wind shear as a source of perturbation in the context of the GWB. The simulations yield results fundamentally aligned with the observed characteristics of ionospheric irregularities in the 2018 event by considering the wind shear as the disturbance source. This study provides supplementary insights into the perturbation sources involved in shaping irregularities within the Martian ionosphere and presents valuable information about the coupling between the Martian ionosphere and the lower atmosphere.

A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism

Accurate power load forecasting enables Independent System Operators (ISOs) to precisely quantify the demand patterns of users and achieve efficient management of the smart grid. However, with the increasing variety of power consumption patterns, the power load data displays increasingly irregular characteristics, which posing great challenges for accurate load forecasting. In order to solve above problem, a novel power load forecasting system is proposed based on data denoising, customized deep learning and weighted linear error correction. Specifically, we first proposed an improved optimization algorithm IGWO-JAYA which enhanced the Grey Wolf Optimizer (GWO) algorithm by using Halton low-discrepancy sequence and the mechanism of JAYA algorithm. In data denoising, the proposed optimizer was employed to optimize the Variational Mode Decomposition (VMD), enabling data-driven intelligent denoising. The customized deep learning framework contained multi-layer Convolution Neural Network (CNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Multi-Head Attention mechanism. The effective integration of these layers can significantly improve the capacity for nonlinear fitting of deep learning. In weighted linear error correction, the IGWO-JAYA algorithm was employed to determine the appropriate weight for point forecasting values and residual forecasting values. By weighting them, the forecasting precision has been further enhanced. To verify the forecasting ability of the system, we conducted experiments on power load datasets from four states in Australia and found that it has the best performance compared with all rivals. In the discussion, we demonstrated the convergence efficiency of the IGWO-JAYA algorithm by CEC test function.

Correlation between rate of TEC index and positioning error during solar flares and geomagnetic storms using navigation with Indian constellation receiver measurements

Abstract The real-time position accuracy of the Global Navigation Satellite System (GNSS)/Navigation with Indian Constellation (NavIC) receiver is limited by the dynamic behavior of the ionosphere, particularly in adverse conditions like solar flares and geomagnetic storms. The NavIC satellites broadcast dual coherent radio beacon signals on L5 (1,164.5 MHz) and S (2,472.5 MHz) bands for providing position, velocity, and timing services in all weather conditions. The Total Electron Content (TEC) and Rate of TEC Index (ROTI) are the potential indicators for characterizing the ionosphere and its irregularities. In this research work, the TEC and ROTI are computed from the code and carrier phase observations of the NavIC receiver located at Kurnool low latitude station (15.79° N, 78.07° E) with geomagnetic coordinates (7.30° N, 151.65° E). This paper presents a statistical study of TEC, ROTI, and the correlation between ROTI and NavIC positioning error during highly intense solar flares (X9.3 and X2.2) and geomagnetic storm conditions. Compared to quiet days mean TEC, the enhancement is 3 TECU due to X9.3 flares, and the maximum peak of TEC on storm day (September 8, 2017) is 80.92 TECU. Moreover, the correlation coefficient between ROTI and position error is 0.76 on a quiet day (September 4, 2017), 0.54 on an intense solar flares day (September 6, 2017), and 0.24 on a storm day (September 8, 2017), this indicates positional accuracy degradation on a geomagnetic storm day. The outcome of this research work would be helpful for investigating characteristics of the northern low latitude ionospheric irregularities and, in turn, useful for developing suitable ionospheric nowcasting/prediction models for GNSS applications.

The stochastic characteristics of wideband-wavelength track irregularity on Chinese HSRs and its application to dynamic wheel–rail interaction

Track irregularities are the major excitations which lead to unfavourable wheel–rail dynamic interaction. In this work, the stochastic characteristics of longitudinal level irregularity on Chinese ballastless high-speed railway (HSR) are analysed based on a series of large-scale measurement campaigns on various arterial lines in Chinese HSR network. The power spectrum density (PSD) of longitudinal level irregularities of various track types is obtained, covering short and middle-to-long wavelength range, as well as the periodical irregularities caused by characteristic lengths of track components. An irregularity spectrum is proposed to represent the stochastic characteristics of the longitudinal level irregularities. Unlike existing irregularity spectra which represent the short and middle-to-long wavelength irregularities separately, the proposed spectrum covers a wide range of wavelength from 0.002 m to 100 m. A vehicle–track model which couples flexible wheelsets and the ballastless track is employed to demonstrate the suitability of the spectrum. It is shown that the proposed spectrum is more suitable than the latter to represent Vehicle–Track Interaction (VTI) on Chinese HSRs. The fitted short-wave spectrum and the proposed wideband wavelength spectrum therefore provide more appropriate representatives of track irregularities for accurate modelling of VTI on Chinese HSRs and railway lines which adopt similar technologies.

Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning

AbstractThis study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D‐CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non‐equinox months exhibit the highest equatorial PI magnitude over the American‐African longitudinal sector, contrary to the expected higher Rayleigh‐Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north‐south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance‐related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D‐CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.

Driving pressure of respiratory system and lung stress in mechanically ventilated patients with active breathing

BackgroundDuring control mechanical ventilation (CMV), the driving pressure of the respiratory system (ΔPrs) serves as a surrogate of transpulmonary driving pressure (ΔPlung). Expiratory muscle activity that decreases end-expiratory lung volume may impair the validity of ΔPrs to reflect ΔPlung. This prospective observational study in patients with acute respiratory distress syndrome (ARDS) ventilated with proportional assist ventilation (PAV+), aimed to investigate: (1) the prevalence of elevated ΔPlung, (2) the ΔPrs-ΔPlung relationship, and (3) whether dynamic transpulmonary pressure (Plungsw) and effort indices (transdiaphragmatic and respiratory muscle pressure swings) remain within safe limits.MethodsThirty-one patients instrumented with esophageal and gastric catheters (n = 22) were switched from CMV to PAV+ and respiratory variables were recorded, over a maximum of 24 h. To decrease the contribution of random breaths with irregular characteristics, a 7-breath moving average technique was applied. In each patient, measurements were also analyzed per deciles of increasing lung elastance (Elung). Patients were divided into Group A, if end-inspiratory transpulmonary pressure (PLEI) increased as Elung increased, and Group B, which showed a decrease or no change in PLEI with Elung increase.ResultsIn 44,836 occluded breaths, ΔPlung ≥ 12 cmH2O was infrequently observed [0.0% (0.0–16.9%) of measurements]. End-expiratory lung volume decrease, due to active expiration, was associated with underestimation of ΔPlung by ΔPrs, as suggested by a negative linear relationship between transpulmonary pressure at end-expiration (PLEE) and ΔPlung/ΔPrs. Group A included 17 and Group B 14 patients. As Elung increased, ΔPlung increased mainly due to PLEI increase in Group A, and PLEE decrease in Group B. Although ΔPrs had an area receiver operating characteristic curve (AUC) of 0.87 (95% confidence intervals 0.82–0.92, P < 0.001) for ΔPlung ≥ 12 cmH2O, this was due exclusively to Group A [0.91 (0.86–0.95), P < 0.001]. In Group B, ΔPrs showed no predictive capacity for detecting ΔPlung ≥ 12 cmH2O [0.65 (0.52–0.78), P > 0.05]. Most of the time Plungsw and effort indices remained within safe range.ConclusionIn patients with ARDS ventilated with PAV+, injurious tidal lung stress and effort were infrequent. In the presence of expiratory muscle activity, ΔPrs underestimated ΔPlung. This phenomenon limits the usefulness of ΔPrs as a surrogate of tidal lung stress, regardless of the mode of support.

Characterisation of Electrical Power Systems Based on Electrical Curves and their properties

Due to the proliferation of renewable energy systems, the study of voltage and frequency stability is a crucial aspect. Recently, this problem has been approached from a purely geometrical point of view with interesting results. The present work investigates the properties of the so-called electrical curves described by arbitrary voltage or current vectors in Euclidean spaces. Through the invariants of these curves, certain indices can be constructed to detect abnormal operation or irregular characteristics in electrical power systems. Different scenarios and examples have been solved in this work to support the proposed theory.

Data analysis solutions to improve blasting efficiency in mining

Purpose. To build an identification model to determine the appropriate explosion parameters value with reasonable cost. To optimize blasting works design at each blast site with the calculation of delay time based on the model used. Methodology. Blasting for mining is an issue of utilizing the most of explosive energy in order to achieve the highest smashing ability and the smallest level of vibration. In modern explosive techniques, the total amount of explosive is divided into parts to detonate after differential time intervals. This solution creates interference between stress waves causing the durability of rock structures to be reduced and the blasting efficiency to be improved. Although delay time plays an important role in this method, so far its value is still calculated empirically at the blast site due to the irregular characteristic of the rock environment. Technical design parameters for explosion including delay time has been also determined from smart analysis software and simulation models. However, their applicability is limited because of high payments and strict implementation conditions. The method proposed in the study overcomes this drawback and its effectiveness is proven by the process of analyzing experimental data at Nui Beo Mountain of Vietnam. Findings. An identification model is developed based on the information including: explosion delay time value; average propagation speed of the vibration wave; maximum amplitude of the vibration wave. Originality. Basic data analysis software and an artificial neural network model are used. A new data analysis algorithm is established to determine the optimal explosion delay time value. Practical value. A simple and reasonable-cost solution is formed for improving the efficiency of blasting in mining.

Spatial-Temporal evolution characteristics of pre-Stage-stall in a centrifugal compressor with a vaneless diffuser and a volute

The continuous throttling process is of great interest for compressor designers. Previous experimental studies mainly consider the temporal evolution characteristics of the pre-stage stall at the impeller inlet or outlet regions. In this study, we explore the spatial-temporal evolution characteristics of pre-stage-stall using a pressure array consisting of 15 high-response sensors. The dynamic evolution characteristics of the pressure signal on the time and frequency domains and the underlying unsteady flow structure evolution during the transition process were analyzed. We find that the evolution characteristics of pressure irregularity varied with the streamwise positions. The evolution process from the pre-stage stall point to the stage stall point can be divided into three stages based on the disturbance scale and pressure fluctuations, namely (I) the coexistence of the impeller RI, the mild impeller stall, and the mild diffuser stall, (II) the interaction of stall cell and RI disturbance, (III) a single large stall cell. In stage I, the streamwise influential regions of the stall cells are mainly confined in the impeller passage. In stage II, the stall cell expands upstream and gradually interacts with the RI disturbance. The wavelength of RI-related disturbance increases while the related pressure fluctuation decreases. In stage III, several stall cells merge into one large cell.

Research of the track irregularity spectrum of Shanghai high-speed transrapid demonstration line

High-speed maglev system is a hot research topic nowadays in the field of high-speed ground transportation. Characteristics of the track irregularity of high-speed maglev system is quite different due to its contactless vehicle-rail relationship and segmented track compared with traditional railway. However, research on maglev track irregularity is still insufficient. This paper summarises, for the first time, the track inspection data collected on Shanghai Maglev Demonstration Line (Shanghai Line) since 2006, and describes the main filtering methods and parameters for acceleration and gap signals, taking into account the characteristics of maglev track. Track irregularity is then determined using the inertial measurement principle. The measured track spectrum on Shanghai Line calculated by the Welch method obeys a chi-square distribution with 2 degrees of freedom, indicating that different track qualities can be transformed by probability methods according to the research requirements. Based on the samples obtained over the past 15 years, and referring to the seven-parameter model of the railway system, a high-speed maglev track spectrum is obtained by the fitting method, parameters of which are obtained by applying the damped least squares method. Preliminary vehicle dynamic analysis is conducted utilising the inverted excitations derived from the measured spectrum of the Shanghai Line.

Time-frequency characteristics investigation and numerical reconstruction of seismic-induced track irregularity for high-speed railway bridge

In recent years, the construction of high-speed railway has gradually extended to area with high seismic intensity. Seismic-induced track irregularity in high-speed railways is essential for evaluating high-speed railway safety upon earthquakes. Signals of seismic-induced track irregularity typically exhibit non-stationary characteristics, but research on the time–frequency characteristics of seismic-induced track irregularity on high-speed railways is currently limited. To ensure that high-speed train can continue to transport medical supplies and disaster rescuers after the earthquake, this study uses the simply supported beam bridge of the CRTS II ballastless track high-speed railway as the research object. It builds a sample database of post-earthquake residual track geometric irregularity based on the earthquake's randomness. In addition, this research investigates the effectiveness of different signal analysis methods, such as the Short time Fourier transform and wavelet transform, in reconstructing track geometric irregularity. A method of constructing the representative sample of seismic-induced track irregularity based on confidence level is proposed. The results indicated that: (1) Seismic-induced track irregularity in high-speed railway bridges is dominated by alignment irregularity; (2) The amplitude of the time–frequency spectrum for seismic-induced track irregularity in high-speed railway bridges decreases from low to high frequencies, and does not contain a significant high-frequency component; (3) Compared to Short time Fourier transform, wavelet transform is more effective in reconstructing post-earthquake track geometric irregularity; (4) Seismic-induced track geometric irregularity based on the wavelet transform can be a valuable indicator for evaluating the performance of a high-speed train on a bridge after an earthquake.

A deep learning sequence model based on self-attention and convolution for wind power prediction

Renewable energy has garnered significant attention recently due to its sustainable nature and minimal environmental footprint. Among various sources, wind energy emerges as one of the most promising. However, its inherently unpredictable and irregular characteristics pose challenges to forecasting wind power generation. This study introduces a wind power prediction model that employs self-attention to capture long-range relationships and convolutional layers to understand the local temporal dynamics within time-series data. Unlike traditional deep learning sequence models, such as the recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), our method adeptly integrates both global and local insights. We validate the model’s efficacy through experiments on three datasets. The results consistently show our model’s superior performance over alternative methods. Further, we conduct comprehensive experiments to analyze our proposed model.

Dynamic Responses of Train-Symmetry-Bridge System Considering Concrete Creep and the Creep-Induced Track Irregularity

This work considers the influence of concrete creep on track irregularities and establishes the dynamic motion equation of the train-track-bridge coupling system. The track irregularity is obtained by superposition of the initial geometric irregularity and additional geometric irregularity of the steel rail caused by creep. When high-speed railway trains pass through bridges; the vertical acceleration and vertical displacement of continuous beam bridges are related to the train’s operating speed, and the influence of creep camber is relatively small. At the same time, considering the randomness of track irregularities, the dynamic responses of the train track bridge coupling system under the action of random track irregularities are analyzed, and the dynamic responses of trains at different operating speeds are obtained. The deterministic and uncertain dynamic responses of the train track bridge system were compared and analyzed to verify the accuracy of the Karhunen Loéve expansion (KLE)-Point estimate method (PEM) calculation results. The results indicate that the random characteristics of track irregularities have a significant impact on train dynamic response. Based on the random system vibration analysis and considering the safety and comfort indicators of high-speed railway trains, the creep deformation limit of a continuous beam bridge with a length of 48 m + 80 m + 48 m is obtained to be 19 mm. This is the first time that the dynamic responses of train-symmetry-bridge system are calculated by considering concrete creep and the creep-induced track irregularity, which has certain significance for understanding the dynamics of train -bridge system. In addition, the proposed creep threshold is also of great significance to ensure the safety of traveling.