Numerical analysis of dynamic response of vehicle–bridge coupled system on long-span continuous girder bridge

To systematically study the vehicle-bridge coupled dynamic response and its change rule with different parameters, a vehicle model with seven degrees of freedom was built and the total potential energy of vehicle space vibration system was deduced. Considering the stimulation of road roughness, the dynamic response equation of vehicle-bridge coupled system was established in accordance with the elastic system principle of total potential energy with stationary value and the ＂set-in-right-position＂ rule. On the basis of the self-compiled Fortran program and bridge engineering, the dynamic response of long- span continuous girder bridge under vehicle load was studied. This study also included the calculation of vehicle impact coefficient, evaluation of vibration comfort, and analysis of dynamic response parameters. Results show the impact coefficient changes with lane number and is larger than the value calculated by the ＂general code for design of highway bridges and culverts （China）＂. The Dieckmann index of bridge vibration is also related to lane number, and the vibration comfort evaluation is good in normal conditions. The relevant conclusions from parametric analyses have practical significance to dynamic design and daily operation of long-span continuous girder bridges in expressways. Safety and comfort are expected to improve significantly with further control of the vibration of vehicle-bridge system.

Dynamic Response Impact of Vehicle Braking on Simply Supported Beam Bridges with Corrugated Steel Webs Based on Vehicle-Bridge Coupled Vibration Analysis

A novel approach for analyzing coupled vibrations between vehicles and bridges is presented,taking into account spatiotemporal effects and mechanical phenomena resulting fromvehicle braking.Efficient modeling and solution of bridge vibrations induced by vehicle deceleration are realized using this method.The method’s validity and reliability are substantiated through numerical examples.A simply supported beam bridge with a corrugated steel web is taken as an example and the effects of parameters such as the initial vehicle speed,braking acceleration,braking location,and road surface roughness on the mid-span displacement and impact factor of the bridge are analyzed.The results show that vehicle braking significantly amplifies mid-span displacement and impact factor responses in comparison to uniform vehicular motion across the bridge.Notably,the influence of wheelto-bridge friction forces is of particular significance and cannot be overlooked.When the vehicle initiates braking near the middle of the span,both the mid-span displacement and impact factor of the bridge exhibit substantial increases,further escalating with higher braking acceleration.Under favorable road surface conditions,the midspan displacement and the impact factor during vehicle braking may exceed the design values stipulated by codes.It is important to note that road surface roughness exerts a more pronounced effect on the impact factor of the bridge in comparison to the effects of vehicle braking.

Analysis of Safety Assessment and Testing of Heavy Traffic Vehicles on Old Bridges Without Data

This article presents a real-life project that aimed to evaluate the safety of traffic vehicles on old bridges without any prior data.The project involved various safety inspections,including conventional,static,and dynamic load inspections and safety assessments.After conducting these tests,it was concluded that the structure of the old bridge is relatively safe,with only a few bumps.The bridge could function normally following appropriate treatment.The analysis provides valuable insights into the assessment of the quality and safety of such bridges to ensure the safe driving of heavy vehicles.

Influence of span-to-depth ratio on dynamic response of vehicle-turnoutbridge system in high-speed railway

For high-speed railways,the smoothness of the railway line significantly affects the operational speed of trains.When the train passes through the turnout on a long-span bridge,the wheel-rail impacts caused by the turnout structure irregularities,and the instability arising from the bridge's flexural deformation lead to a strong coupling effect in the vehicle-turnout-bridge system.This significantly affects both ride comfort and operational safety.For addressing this issue,the present study considered a long-span continuous rigid-frame bridge as an example and established a train-turnout-bridge coupled dynamic model of high-speed railway.Utilizing a selfdeveloped dynamic simulation program,the study analysed the dynamic response characteristics when the train passes through the turnouts on the bridge.It also investigated the influence of different span-to-depth ratios of the bridge on the vehicle dynamic response when the train passes through the main line and branch line of turnouts and then proposed a span-to-depth ratio limit value for a long-span continuous rigid-frame bridge.The research findings suggest that the changes in the span-to-depth ratio have a relatively minor impact on the train’s operational performance but significantly affect the dynamic characteristics of the bridge structure.Based on the findings and a comprehensive assessment of safety indicators,it is advisable to establish a span-to-depth ratio limit of 1/4500 for a long-span continuous rigid-frame bridge.

Smartphone-based bridge frequency identification using vehicle contact-point response

Bridge frequency(BF)identification using the vehicle scanning method has attracted considerable attention during the last two decades.However,most previous studies have adopted unrealistic vehicle models,thus finding limited practical applications.This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model.The said response can be inferred from the vehicle body response measured by a smartphone.For realizing practical applications,this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner.Numerical and experimental investigations are performed to verify the effectiveness of the proposed method.In particular,the robustness of this method is investigated numerically against various factors,including the vehicle speed,bridge span,road roughness,and bridge type.Furthermore,laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity,where anomalous data are detected and eliminated.Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy.

An Analysis of the Dynamic Behavior of Damaged Reinforced Concrete Bridges under Moving Vehicle Loads by Using the Moving Mesh Technique

This work proposes a numerical investigation on the effects of damage on the structural response of Reinforced Concrete(RC)bridge structures commonly adopted in highway and railway networks.An effective three-dimensional FE-based numerical model is developed to analyze the bridge’s structural response under several damage scenarios,including the effects of moving vehicle loads.In particular,the longitudinal and transversal beams are modeled through solid finite elements,while horizontal slabs are made of shell elements.Damage phenomena are also incorporated in the numerical model according to a smeared approach consistent with Continuum Damage Mechanics(CDM).In such a context,the proposed method utilizes an advanced and efficient computational strategy for reproducing Vehicle-Bridge Interaction(VBI)effects based on a moving mesh technique consistent with the Arbitrary Lagrangian-Eulerian(ALE)formulation.The proposed model adopts a moving mesh interface for tracing the positions of the contact points between the vehicle’s wheels and the bridge slabs.Such modeling strategy avoids using extremely refined discretization for structural members,thus drastically reducing computational efforts.Vibrational analyses in terms of damage scenarios are presented to verify how the presence of damage affects the natural frequencies of the structural system.In addition,a comprehensive investigation regarding the response of the bridge under moving vehicles is developed,also providing results in terms of Dynamic Amplification Factor(DAFs)for typical design bridge variables.

Vehicle-Bridge Interaction Simulation and Damage Identification of a Bridge Using Responses Measured in a Passing Vehicle by Empirical Mode Decomposition Method

To prevent early bridge failures, effective Structural Health Monitoring (SHM) is vital. Vibration-based damage assessment is a powerful tool in this regard, as it relies on changes in a structure’s dynamic characteristics as it degrades. By measuring the vibration response of a bridge due to passing vehicles, this approach can identify potential structural damage. This dissertation introduces a novel technique grounded in Vehicle-Bridge Interaction (VBI) to evaluate bridge health. It aims to detect damage by analyzing the response of passing vehicles, taking into account VBI. The theoretical foundation of this method begins with representing the bridge’s superstructure using a Finite Element Model and employing a half-car dynamic model to simulate the vehicle with suspension. Two sets of motion equations, one for the bridge and one for the vehicle are generated using the Finite Element Method, mode superposition, and D’Alembert’s principle. The combined dynamics are solved using the Newmark-beta method, accounting for road surface roughness. A new approach for damage identification based on the response of passing vehicles is proposed. The response is theoretically composed of vehicle frequency, bridge natural frequency, and a pseudo-frequency component related to vehicle speed. The Empirical Mode Decomposition (EMD) method is applied to decompose the signal into its constituent parts, and damage detection relies on the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component. This technique effectively identifies various damage scenarios considered in the study.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property.The interferometric synthetic aperture radar(In SAR)technology has the advantage of high accuracy in bridge deformation monitoring.This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets(SBAS)In SAR technology and Sentinel-1A data.We analyzed the deformation results combined with bridge structure,temperature,and riverbed sediment scouring.The results are as follows:(1)The Ganjiang Super Bridge area is stable overall,with deformation rates ranging from-15.6 mm/yr to 10.7 mm/yr(2)The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span,which conforms to the typical deformation pattern of a cable-stayed bridge.(3)The sediment scouring from the riverbed cause the serious settlement on the bridge’s east side compared with that on the west side.(4)The bridge deformation negatively correlates with temperature,with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature.The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

Auto-parametric resonance of a continuous-beam-bridge model under two-point periodic excitation:an experimental investigation and stability analysis

The auto-parametric resonance of a continuous-beam bridge model subjected to a two-point periodic excitation is experimentally and numerically investigated in this study.An auto-parametric resonance experiment of the test model is conducted to observe and measure the auto-parametric resonance of a continuous beam under a two-point excitation on columns.The parametric vibration equation is established for the test model using the finite-element method.The auto-parametric resonance stability of the structure is analyzed by using Newmark's method and the energy-growth exponent method.The effects of the phase difference of the two-point excitation on the stability boundaries of auto-parametric resonance are studied for the test model.Compared with the experiment,the numerical instability predictions of auto-parametric resonance are consistent with the test phenomena,and the numerical stability boundaries of auto-parametric resonance agree with the experimental ones.For a continuous beam bridge,when the ratio of multipoint excitation frequency(applied to the columns)to natural frequency of the continuous girder is approximately equal to 2,the continuous beam may undergo a strong auto-parametric resonance.Combined with the present experiment and analysis,a hypothesis of Volgograd Bridge's serpentine vibration is discussed.

Integrated Active Suspension and Anti-Lock Braking Control for Four-Wheel-Independent-Drive Electric Vehicles

This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and anti-lock braking system(ABS).First,a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model.Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed.An ASS-ABS integrated control system is proposed,utilizing an H∞controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation.Finally,the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop(HIL)test platform.The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Alkylene-functionality in bridged and fused nitrogen-rich poly-cyclic energetic materials:Synthesis,structural diversity and energetic properties

From the standpoint of chemical structures,the organic backbones of energetic materials can be classified into aromatic rings,nonaromatic rings,and open chains.Although the category of aromatic energetic compounds exhibits several advantages in the regulation of energetic properties,the nonaromatic heterocycles,assembling nitramino explosophores with simple alkyl bridges,still have prevailed in benchmark materials.The methylene bridge plays a pivotal role in the constructions of the classic nonaromatic heterocycle-based energetic compounds,e.g.,hexahydro-1,3,5-trinitro-1,3,5-triazine(RDX)and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine(HMX),whereas ethylene bridge is the core moiety of state-of-the-art explosive 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane(CL-20).In this context,it is of great interest to employ simple and practical bridges to assemble aromatic and nonaromatic nitrogen-rich heterocycles,thereby expanding the structural diversity of energetic materials,e.g.,bridged and fused nitrogen-rich poly-heterocycles.Furthermore,alkyl-bridged poly-heterocycles highlight the potential for the open chain type of energetic materials.In this review,the development of alkyl bridges in linking nitrogen-rich heterocycles is presented,and the perspective of the newly constructed energetic backbones is summarized for the future design of advanced energetic materials.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Multi-circular formation control with reinforced transient profiles for nonholonomic vehicles:A path-following framework

This article investigates a multi-circular path-following formation control with reinforced transient profiles for nonholonomic vehicles connected by a digraph.A multi-circular formation controller endowed with the feature of spatial-temporal decoupling is devised for a group of vehicles guided by a virtual leader evolving along an implicit path,which allows for a circumnavigation on multiple circles with an anticipant angular spacing.In addition,notice that it typically imposes a stringent time constraint on time-sensitive enclosing scenarios,hence an improved prescribed performance control(IPPC)using novel tighter behavior boundaries is presented to enhance transient capabilities with an ensured appointed-time convergence free from any overshoots.The significant merits are that coordinated circumnavigation along different circles can be realized via executing geometric and dynamic assignments independently with modified transient profiles.Furthermore,all variables existing in the entire system are analyzed to be convergent.Simulation and experimental results are provided to validate the utility of suggested solution.