Random Traffic Flow Research Articles

Research on damage identification of simply supported bridge based on effect size method for vehicle-bridge coupled vibration

Abstract This paper presents a novel bridge damage identification method employing Cohen's d as an effect size indicator, predicated on the detection of bridge damage through the coupled vibration between a vehicle and the bridge. By analyzing the dynamic response of the bridge as a vehicle passes over, this method effectively extracts the modal parameters of the bridge and facilitates the identification of bridge damage. Numerical models of the bridge under various damage conditions, including no damage, mid-span damage, and damage at the 1/4 and 3/4 span locations, have been constructed to substantiate the efficacy and precision of the effect size as a damage indicator. Furthermore, to address the challenge of accurately identifying damage at boundaries, which is often confounded by boundary effects, a boundary subdivision method has been defined for the detection of boundary damage. Through the implementation of a real-bridge test based on indirect measurement technology, the self-vibration frequency of the bridge was successfully extracted. This empirical data was then compared and analyzed against results from ANSYS numerical simulations, thereby validating the practicality and accuracy of the proposed method. In the final analysis, the influence of random traffic flow on the bridge damage identification results was examined. The findings indicate that the vibrations in simply supported beam bridges are intensified due to the impact of random traffic flow, which aids in enhancing the accuracy of damage identification. The introduction of this method provides a new quantitative tool for bridge health monitoring, enabling the rapid and accurate identification of bridge damage without interrupting traffic. It holds significant value for engineering applications in bridge maintenance and safety assessment.

A Literature Survey On Signalized And Non-Signalized Corridors

Random traffic flows and the influence of traffic signals at intersections often cause significant variations in travel times. This has implications for signal control planning and design. This study aims to compare the variability between signalized and unsignalized corridors in Lahore and identify the challenges faced by road users. The study was conducted through a literature survey that collected data on trip purpose, frequency, travel time, and transportation mode choice from various sources. Results show that signalized corridors have lower accident rates and shorter travel times compared to unsignalized corridors. Unsignaled corridors, on the other hand, offer better access and lower fuel costs. Although signalized corridors are more effective in managing traffic flow and improving safety, further research is needed to explore alternative solutions such as roundabouts that can improve mobility and sustainability. The results of this study can serve as a reference for transportation planners and policy makers in designing a more efficient traffic system. This study also suggests the need for further evaluation of traffic control in various urban contexts to optimize safety and efficiency.

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

Identifying damage in long-span self-anchored suspension bridges using characteristic indices

This study presents a methodology for detecting damage in bridge structures using three damage identification indices: the deviation of displacement influence line (DDIL), the deviation curvature of displacement influence line (DCDIL), and the relative rate of change of wavelet packet energy (RES). The proposed approach is based on the principles of displacement influence line and wavelet packet transform. To demonstrate the effectiveness of the three metrics in determining the location of structural damage, a finite element model of the main bridge in Zhengzhou Taohuayu Yellow River Bridge, a three-span twin-tower all-steel girder self-anchored suspension bridge, is evaluated. The study investigates the impact of various factors, such as damage degree, damage location, signal noise, variable speed vehicles, and random traffic flow, on the usefulness of the metrics. The results indicate that all three indices are effective in detecting damage in bridge structures. The DCDIL demonstrates a distinct, abrupt change at the location of damage, while the DDIL is somewhat less effective. Additionally, the RES curve is particularly sensitive to local damage and can identify damage locations far from the bridge span. However, the study notes that the accuracy and reliability of the RES identification indicators decrease non-linearly in the presence of signal noise. The highest values of the RES curve are positively correlated with acceleration and less closely related to uniform acceleration. A vehicle speed of 20 m/s is recommended for identifying the location of bridge structure damage. Finally, the study suggests that random, unevenly spaced traffic enhances vibration and can more precisely localize the damage location and degree of the bridge structure.

Hopf bifurcation control of macroscopic traffic flow model considering vehicle braking effect.

Traffic congestion not only has a great impact on people's travel, but also increases energy consumption and air pollution. The control analysis of the macroscopic traffic flow model considering the vehicle braking effect is particularly important, reflecting the impact on the actual traffic flow density wave, so as to better solve the actual traffic problems. In this paper, based on a speed difference optimization speed model, the micro-macro-variables are transformed into a high-order continuous traffic flow model. Then, a random function considering the physical correlation of random components is added to the high-order continuous traffic flow model to establish a random traffic flow model that can reflect the uncertain behavior of traffic flow acceleration or deceleration. Based on this stochastic traffic model, the existence of Hopf bifurcation and bifurcation control of the traffic flow system model considering stochastic characteristics are derived by using Hopf bifurcation theorem. By Chebyshev polynomial approximation method, the stochastic problem of the system is transformed into the bifurcation control problem of its equivalent deterministic system. A feedback controller is designed to delay the occurrence of Hopf bifurcation and control the amplitude of the limit cycle. Without changing the equilibrium point of the system, the complete elimination of Hopf bifurcation can be achieved by controlling the amplitude of the limit cycle. That is, the feedback controller is used to modify the bifurcation characteristics of the system, such as the bifurcation appearing at the equilibrium point in the control system moves forward, moves backward or disappears, so as to achieve the effect of preventing or alleviating traffic congestion.

Dynamic Response Analysis of Long-Span Bridges under Random Traffic Flow Based on Sieving Method

To overcome the limitations of using time interval division to calculate the bridge impact coefficient (IM), a sieving method has been proposed. This method employs multiple sieves on bridge time–history curve samples to ultimately obtain the bridge impact coefficients. Firstly, CA cellular automata are used to establish different levels of traffic flow fleet models. The random traffic flow–bridge coupling dynamic model is established through wheel–bridge displacement coordination and mechanical coupling relationships based on the theory of modal synthesis. Then, the variation of bridge dynamic time–history curves for different classes of random traffic flow, speed and pavement unevenness parameters are analyzed. The sieving method is applied to screen the extreme points of the dynamic time–history curve of the bridge, enabling the distribution law of the bridge IM to be obtained using the Kolmogorov–Smirnov test (K–S test) and statistical analysis. Finally, the calculated value is then compared with the IM specifications of multiple countries. The results show that the proposed method has high identification accuracy and produces a good inspection effect. The value obtained using the sieving method is slightly larger than the value specified in the US code, 0.33, which is considerably larger than the values specified in other national codes. As pavement conditions deteriorate, the IM of the bridge increases rapidly, especially under Class C and Class D pavement unevenness, which exceed the values specified in various national bridge specifications.

Fatigue reliability analysis for suspenders of a long-span suspension bridge considering random traffic load and corrosion

For suspension bridges, suspenders are critical load transferring components that connect girder to main cable, the safety of the suspenders conduces to maintain the structural integrity of the bridge. The cyclic stress and corrosion environment are the main reasons for the failure of suspenders. To evaluate the fatigue status of suspenders under the combined action of traffic load and corrosion, this paper presented a time-variant fatigue reliability analysis framework based on in-site measured traffic data and corrosive deterioration model for suspenders. First, the refined vehicle parameter models are established by statistical analysis. An elaborate random traffic flow model that involves car-following theory and Monte-Carlo method is developed. Then, a practical FEM-based method is used to simulate vehicle induced response time history. The fatigue loading effects, namely equivalent stress range and corresponding cyclic number are subsequently obtained. Finally, the time-variant fatigue reliability for suspenders considering corrosion and traffic growth is evaluated. A case study was performed for suspenders of an in-service sea-crossing suspension bridge in China. The results indicate that corrosion and growing traffic density would greatly shorten the service life of suspenders of a suspension bridge, among which corrosion has the most significant influence. Fatigue reliability index of the shortest critical suspender at 1/2 mid-span of the case bridge would lower than target value at the 18th service year in the most serious marine corrosion environment, which resulted in references for suspender maintenance and replacement.

Fatigue life evaluation of bridge stay cables subject to monitoring traffic and considering road roughness

Bridge stay cables are susceptible to fatigue damage caused by cyclic traffic loads. However, the effects of road roughness on fatigue assessment are often ignored, even though it is an essential factor that amplifies the response in vehicle–bridge interaction (VBI) systems. To address this gap, this study proposes a framework for cable fatigue assessment considering road roughness. The framework includes the VBI analysis method accounting for road roughness, the generation of random traffic flow, and the evaluation of cable fatigue. A case study based on a typical cable-stayed bridge is conducted. The results show that the proposed framework can effectively and feasibly evaluate cable fatigue. It is found that a poor road surface can reduce the fatigue life of cables, causing up to 4.47 times more damage compared to smooth road conditions. The decline of cable fatigue life caused by road roughness is primarily concentrated on the cables around the main towers, while the other cables are less affected by road roughness. Based on the measured traffic volume under poor road surface conditions, the estimated minimum fatigue life of the cable is 64 years. Moreover, the study indicates that cable fatigue damage rises with increasing traffic volume. These findings highlight the importance of considering road roughness and maintaining bridge decks to prevent the reduction of cable fatigue life.

Research on the longitudinal movement and influence of restraint parameters of the long-span suspension bridge’s main girder

The long-span suspension bridge has suffered from multiple failures of restraints such as supports, dampers, and expansion joints due to excessive accumulated displacement at the girder end. To investigate the main girders’ characteristics in the longitudinal motion of these bridges and the influence of the restraint device on the longitudinal displacement, the motion characteristics were first analyzed based on the measured displacement data of a certain bridge’s main girder; a dynamic finite element model of the bridge’s random traffic flow was then established based on the measured WIM data; combined with the girder end displacement monitoring data, the Euclidean distance was used to verify the correctness and rationality of the proposed model; finally, the model is used to discuss the influence of restraint devices (i.e., viscous dampers and supports) on the main girder in the longitudinal direction. The results show that dynamic loads (such as vehicles and wind) are the principal reasons for huge accumulated travel at the girder end; The viscous damper can effectively control the longitudinal movement; in a certain range, the larger the damping index, the better the control effect of the damper; meanwhile, the friction coefficient of the support also affects the longitudinal motion, but the effect is limited. When the friction coefficient is ≤ 0.03, the girder end’s cumulative displacement gradually decreases with the increasing friction coefficient, and compared to the damping index, the friction coefficient plays a major controlling role in the girder end displacement; when the friction coefficient is &amp;gt;0.03, the damping index plays a major controlling role in the girder end displacement.

Dynamic Amplification Factors of an Arch Bridge Under Random Traffic Flows

Among large-span bridges, arch bridges have relatively high stiffness, which may lead to large dynamic amplification factors (DAFs). The DAFs suggested by current codes mostly originate from common simply supported beam bridges. Previous DAF studies on dynamic vehicle–bridge solutions for arch bridges have mainly focused on one or two side-by-side vehicles. However, complex traffic flows with randomness rather than one or two vehicles act on bridges. DAFs that consider the effects of random traffic flows have not previously been reported. In this study, a random traffic–bridge vibration solution was established to explore the DAFs of arch bridge components. The randomness of the traffic parameters and road roughness was explored. The randomness of the external excitation, including traffic flow and road roughness, resulted in random dynamic responses and DAFs of the arch bridge components. Normal distributions could be used to fit the DAF distributions of each arch bridge component under random vehicle spacing, weight, speed, and road roughness. Under random traffic parameters, the coefficients of variation of the DAFs exceeded the 5% accepted level. The shortest suspenders were more sensitive to the randomness of the traffic flow parameters. Both the mean and coefficient of variation of the DAFs increased with worsening of the road conditions. The influence of the randomness of the road roughness on the DAFs of arch bridges must be considered, particularly for the shortest suspender. The 95% upper confidence limits of the DAFs for all components may be greater than the suggested values in the code.

Fatigue reliability evaluation of reinforced concrete bridges under stochastic traffic loading based on truck weight limits

To investigate the fatigue reliability of reinforced concrete (RC) bridges under stochastic traffic loading and the effectiveness of limiting the truck load, this study presented an analysis method for evaluating the bridge fatigue reliability under the action of random traffic flows based on truck weight limits. Firstly, a simply RC T-beam bridge was selected as an example to illustrate the presented approach, and the traffic-induced stress spectrum of the tensile rebars in the critical girder of the bridge was obtained. Secondly, based on the collected traffic data from two specific areas, the corresponding random traffic flow was simulated respectively, and then calibrated using the weight limit referring to the current regulation and overload violation rates ( VR) to exclude overweight trucks. Finally, based on the S–N curve from Eurocode 3 and Miner’s cumulative damage model, the effects of violation rate, average daily truck traffic ( ADTT) and truck weight limit ( TWL) on the fatigue reliability index of the bridge were studied. The results show that heavy duty trucks will induce serious cumulative fatigue damage, and degrade the durability of RC bridges, while limiting the truck weight can obviously slow down the reduction of the fatigue reliability index. Both the ADTT and the VR can significantly affect the fatigue reliability of the bridge. More specifically, the bridge fatigue reliability index shows a clear downward trend with the increase in the ADTT and the VR. Furthermore, the performance to satisfy the target fatigue reliability of the bridge was compared between TWL for each type of trucks and unified TWL for all the trucks, and then the unified TWL for all the trucks are determined to ensure the target reliability index of the RC bridge after 100 years’ service life.

Joint Computation Offloading and Resource Allocation for Edge-Cloud Collaboration in Internet of Vehicles via Deep Reinforcement Learning

Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this article, we consider a random traffic flow and dynamic network environment scenario where MEC and CC are collaborated for processing delay-sensitive and computation-intensive tasks in IoV. We study the joint optimization of computation offloading and resource allocation (CORA) with the objective of minimizing the system cost of processing tasks subject to the processing delay and transmission rate constraints. To attack the challenges brought by the dynamic environment, we use the Markov decision process model for formulating the dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to deal with high-dimensional and continuous states and action spaces. Then, we design a CORA algorithm, which is able to effectively learn the optimal scheme by adapting to the network dynamics. Extensive simulation experiments are conducted, in which we compare the CORA algorithm with both non-DRL algorithms and DRL algorithms. The experimental results show that the CORA algorithm outperforms others with excellent training convergence and performance in processing delay and processing cost.

Prediction of ground-borne vibration from random traffic flow and road roughness: Theoretical model and experimental validation

With the densification and closer distance to buildings of road traffic network in urban areas, the assessment and control of environmental vibrations becomes the focus of attention, in which the prediction of traffic flow-induced ground vibration is very critical. However, few studies have been reported on the theoretical prediction method of traffic flow-induced ground vibration from the perspective of stochastic theory. The present work contributes to the perfection and innovation of the current methodologies for the prediction of traffic flow-induced ground vibration. In this paper, a semi-analytical and semi-numerical method for predicting traffic flow induced ground vibrations is developed, in which the random effect generated from traffic flow and road roughness is considered by establishing the vehicle-road-soil coupling model. Aiming to predict the long-term vibration and transient vibration induced by road traffic flow, the average and the adverse velocity levels are used as the evaluation indicators. The effectiveness of the proposed method is validated by an on-site experiment, and the characteristics of ground vibrations are investigated in time and frequency domains, respectively. The results demonstrate that the proposed method can effectively predict the road traffic flow-induced ground vibrations, and the randomness of both traffic flow and road roughness cannot be neglected. For the experimental site, the main frequency band of ground vibrations concentrates on 7–25 Hz, and the predominant center frequency is 12.5 Hz, resulting from the resonance between the overlapping dominant frequency bands of the dynamic vehicle loads and the natural frequency of the soil.

DRL-Based Computation Offloading With Queue Stability for Vehicular-Cloud-Assisted Mobile Edge Computing Systems

Computation offloading is considered as a promising method to improve computation performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry applications to Mobile Edge Computing (MEC) servers. However, due to the dynamic environment of Intelligent Transportation Systems, it's challenging to develop an effective offloading scheme that ensures stable operation of the system. Furthermore, IVs only offload tasks to MEC servers or parked vehicles, how to take full advantage of resources in a random traffic flow is a main challenge. In this paper, we propose a vehicular-cloud-assisted MEC network in urban cities to fully explore the idle resources and ensure the stability of task queue. Specifically, we develop a novel virtual platform based on deep neural network that predicts vehicles trajectory and creates vehicular cloud to integrate computation resources. We then design a vehicular-cloud-assisted offloading scheme that aims to maximize task throughput in continuous time slots subject to multi-constraint. To address the coupling among multiple variables and different time slots, we put forward a lightweight framework to apply the Lyapunov optimization in combination with deep reinforcement learning. In particular, we apply Lyapunov drift-plus-penalty approach to decompose the initial problem into per-slot subproblems and control the long-term task queue stability. To further reduce the computation complexity, we decouple the subproblem into three processes, and the deep Q-network is put forward to solve it. Extensive simulations demonstrate that under different task arrivals, our proposed scheme can achieve the task queue stability within 10 time slots with a low transmission power.

Study on Deflection-Span Ratio of Cable-Stayed Suspension Cooperative System with Single-Tower Space Cable

This study uses the wind–vehicle–bridge coupling vibration analysis method to investigate the bridge stiffness problem of a large-span cable-stayed-suspension cooperative system. On the basis of the particle-damping-spring vehicle model, the TMeasy surface contact tire model is introduced, and a set of universal wind–vehicle–bridge coupling analysis algorithm is built in the framework of the whole process iterative method. Based on the Latin supercube sampling principle, random traffic flow is generated and loaded onto bridge structures with different stiffness conditions to analyze the driving comfort and safety under each stiffness condition. Combining the specification requirements, engineering experience, and research results, the vertical stiffness limit applicable to the bridge of the highway cable-stayed-suspension collaborative system is proposed. Existing engineering experience shows that the vertical deflection-to-span ratio of a cable-stayed bridge under live load is distributed between 1/400 and 1/1600, and the vertical deflection span ratio under the action of lane load is recommended based on numerical analysis.

Random Traffic Flow Simulation of Heavy Vehicles Based on R-Vine Copula Model and Improved Latin Hypercube Sampling Method

The rationality of heavy vehicle models is crucial to the structural safety assessment of bridges. To establish a realistic heavy vehicle traffic flow model, this study proposes a heavy vehicle random traffic flow simulation method that fully considers the vehicle weight correlation based on the measured weigh-in-motion data. First, a probability model of the key parameters in the actual traffic flow is established. Then, a random traffic flow simulation of heavy vehicles is realized using the R-vine Copula model and improved Latin hypercube sampling (LHS) method. Finally, the load effect is calculated using a calculation example to explore the necessity of considering the vehicle weight correlation. The results indicate that the vehicle weight of each model is significantly correlated. Compared to the Monte Carlo method, the improved LHS method better considers the correlation between high-dimensional variables. Furthermore, considering the vehicle weight correlation using the R-vine Copula model, the random traffic flow generated by the Monte Carlo sampling method ignores the correlation between parameters, leading to a weaker load effect. Therefore, the improved LHS method is preferred.

Reference Point-Free Measurement of Bridge Dynamic Deflection by Fusing Hydraulic Leveling and Accelerometer Signals

Structural health monitoring (SHM) is widely applied to assess the service condition of bridges. Deflection measurements are essential to determine a bridge’s performance, and in particular, dynamic deflection is increasingly required in SHM. However, continuous and high-precision measurements of the absolute dynamic deflection are challenging without a reference point placed at a distance from the bridge. We proposed a reference point-free dynamic deflection measurement system consisting of a level sensor and an accelerometer. The level sensor measures the low-frequency deflection components, while the accelerometer measures the high-frequency deflection components. We redesigned the level sensor from the hydrostatic level sensor and further provided a correction method to achieve the dynamic deflection measurement. A numerical algorithm fuses the signals from the level sensor and accelerometer to obtain the absolute dynamic deflection, and key parameters are calculated. The accuracy of the measurement system was tested in the laboratory, and the sensor results were similar to the ones obtained by the laser test under simple harmonic and random excitations. Field tests conducted on a T-shaped rigid frame bridge with random traffic flow indicate that the system can achieve continuous high-precision deflection measurements of bridges loaded by traffic flow.

Fatigue assessment and crack propagation of floorbeam cutout in orthotropic bridge decks

Based on the fatigue cracking of floorbeam cutout in a bridge, the field testing under random traffic flows was firstly conducted to investigate the fatigue crack initiation, and then a multi-scale numerical model, based on extended finite element method (XFEM), was established to reveal the propagation mechanism of crack growth. It was found that floorbeam cutout exhibits a complex three-dimensional deformation state under wheel loads, leading to an I-II-III mixed crack growth mode. The crack propagation direction is normal to the free edge of floorbeam cutout, which is the crack propagation direction at initiation. Increasing floorbeam thickness can facilitate preventing the crack propagation of floorbeam cutout.

Fatigue assessment of steel-UHPC composite deck with a thin polymer overlay in a long-span suspension bridge under static and random traffic loads

Steel-ultra-high performance concrete (UHPC) composite deck emerges as a novel structure that is promising to improve the fatigue performance of orthotropic steel deck (OSD). However, a long-span suspension bridge has a strict requirement on the self-weight of a steel-UHPC composite deck because it is susceptible to dead load variations. This study evaluates the fatigue performance of the steel-UHPC deck with a thin polymer overlay (TPO) in the Yichang Yangtze River Highway Bridge, a long-span suspension bridge, under static load and random traffic flow. The measured strains at the fatigue-prone details were employed to calculate the equivalent stress ranges and total fatigue life of the steel-UHPC deck. A three-dimensional finite element model was established and validated by experimental measurements to evaluate the steel-UHPC deck’s stiffness and the TPO layer’s crack resistance. The differences between the stress values predicted by the validated model and the experimentally measured ones were around 5%. The results showed that the steel-UHPC deck with TPO significantly reduced fatigue-prone details’ equivalent stress ranges without increasing the dead load. The maximum stress reduction at various details reached 62%, and the total fatigue life at each detail in the steel-UHPC deck reached infinite life. The TPO layer has enough crack resistance in case the deck is overloaded by 50%. Compared with the original deck, the steel-UHPC composite deck improves the OSD’s local stiffness and reduces relative deformation between structural components, thus reducing the stress ranges at fatigue-prone details and improving the bridge deck’s fatigue performance. The results can provide a reference for the design and retrofit of the deck overlay of the long-span suspension bridge with OSD.

Dynamic real-time reliability prediction of bridge structures based on Copula–BHDLM and measured stress data

This study presents a novel dynamic real-time reliability prediction method by considering the time-varying correlation among the data at multiple measurement points. The Bayesian Hilbert dynamic linear model (BHDLM) is developed to predict the stress response, and the dynamic real-time reliability is then predicted by using the Pair-Copula theory. To illustrate the feasibility of the proposed method, the dynamic real-time reliability of a steel–concrete composite girder bridge based on the simulated stress data due to a single vehicle with various weights and random traffic flow load is predicted. Finally, the dynamic reliability of a real bridge based on the monitoring stress data is predicted. The results show that the proposed BHDLM is effective for predicting coupled stresses, and the dynamic real-time reliability can be predicted. In addition, the predicted dynamic reliability of the real bridge demonstrates that the influence of the data correlation should be considered.