Vehicle Load Research Articles

Stability Analysis of Unsaturated Soil Pit Under Vehicle Load

Stability Analysis of Unsaturated Soil Pit Under Vehicle Load

Damage analyze of prestressed small box girder bridges under explosive loading of vehicle

Damage analyze of prestressed small box girder bridges under explosive loading of vehicle

Ramp Start and Speed Control of Self-Driving Commercial Vehicles under Ramp and Vehicle Load Uncertainty

In order to improve the performance of self-driving commercial vehicles for half-hill starting, a ramp control strategy based on the back-slip speed corresponding to the parking moment is proposed. Firstly, the longitudinal dynamics model of the vehicle is established, the force of the vehicle on the ramp is analyzed, and the rear slip speed of the vehicle is matched with the parking moment, and finally the target speed is tracked based on the sliding-mode controller, and in order to validate the validity of the method, two comparative algorithms of the pure PI controller and the proportional gain controller based on the back-sliding speed corresponding to the resting moment are designed for comparative experiments, and the data results show that the control strategy based on the resting moment corresponding to the backsliding speed of the sliding mode ramp start control strategy can stably complete the ramp start under different weights and different slopes, and greatly reduce the backsliding distance of the vehicle.

Identification of bridge influence line and multiple-vehicle loads based on physics-informed neural networks

Influence lines (ILs) and vehicle loads identification are critical in the design, health monitoring, and damage detection of bridges. Traditionally, the approach used in most existing literature has been to solve the system of equations directly. However, these approaches require complex calculations such as matrix decomposition and regularization coefficient optimization, making them difficult to implement. In addition, there are difficulties in obtaining accurate axle information and effectively separating the bridge response due to each vehicle. Thus, the improvement of identification algorithms for ILs and multi-vehicle loads remains of significant importance. To address these issues, this paper presents a novel approach that integrates prior physical equations and neural networks. This is achieved by integrating the equation that reflects the relationship between axle loads and bridge response into the neural network, utilizing existing methods for acquiring axle information of vehicles. To validate the effectiveness of the proposed method, it was first applied to theoretical and simulation data. The study then investigated the impact of noise and dynamic effects on the accuracy of the results, as well as the range of the neural network layers and sampling intervals. Finally, the method was implemented for identifying multiple-vehicle loads. The findings of the study confirm the feasibility and numerical stability of the proposed approach. The proposed method eliminates the need for complex computational processes, including matrix decomposition, diagonalization, regularization coefficient optimization, and solution vector smoothing fitting. As a result, the implementation of the algorithm is significantly less challenging, and identification accuracy is improved. It is important to note, however, that the proposed method is relatively more time-consuming due to the iterative learning and training required by the neural network.

The effect of pre-vulcanized latex usage on Marshall characteristics and stiffness modulus in hot mix asphalt wearing course (AC-WC) mixtures

Flexible pavement itself uses asphalt binder as a binding material between aggregates, but with the increasing number of vehicle loads, the ability of flexible pavement needs to be improved. A rubber is a natural polymer material that can be used to improve asphalt mixtures’ performance. This paper aims to analyze the effect of using pre-vulcanized latex on characteristics in hot mix asphalt wearing course mixtures. Pre-vulcanized latex is used as a substitute material for asphalt binder at levels of 7% and 9% by weight. Based on Marshall test results, the AC-WC mixture using pre-vulcanized latex achieved Marshall stability of 11.63 kN or increased by 32.28% at 7% content, while at 9%, it resulted in Marshall stability of 10.69 kN or increased by 21.06%, compared to the specification limit, which is 8.83 kN. The stiffness modulus test results of the asphalt mixture showed that at a temperature of 25°C, there was an increase of 17.46% and 28.26%, respectively, when using pre-vulcanized latex at levels of 7% and 9%. These findings indicate that the use of pre-vulcanized latex as a partial replacement for asphalt has a positive impact on temperature changes in the pavement material.

Finite Element Analysis on the Behavior of Solidified Soil Embankments on Piled Foundations under Dynamic Traffic Loads

Most research conducted so far has primarily focused on pile-supported gravel embankments. The ability of solidified soil used as an embankment filling material has been verified, and a clear view on the performance of solidified soil embankments on piled foundations is rather limited. The three-dimensional unit cell models of pile-supported embankments are conducted to investigate the performance of solidified soil embankments in comparison to gravel embankments under static and dynamic loads. Then, a systematic parametric analysis is performed to investigate the effects of various factors, including the cohesion and friction of solidified soil, the velocity and wheel load of vehicles, the pile spacing, the height of embankments. The results show that, compared with the results of gravel embankments, the heights of the outer soil arch plane in solidified soil embankments reduces under static and dynamic loads, and the piles bear more load. In addition, the total settlements of solidified soil embankments decrease with increasing cohesions, and there is an economical cohesion of 25 kPa. The vehicle wheel load, pile spacing, and the height of embankment significantly influence the load transfer mechanism and total settlement of solidified soil embankment, while the friction angles and velocities have little effect on the total settlements and vertical stress. The relationship between the soil arch height and various parameters in solidified soil embankments is established by multiple regression analysis. This investigation highlights the advantage of solidified soil in reducing total settlement and provides an insightful understanding of the load transfer mechanism of solidified soil embankment on piled foundation.

Three-stage energy trading framework for retailers, charging stations, and electric vehicles: A game-theoretic approach

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer’s strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market.

Dynamic Response Analysis of Moving Loads over Tension Cables of Connected Tower Cable-Stayed Bridge

Due to its strong crossing ability, cable-stayed bridges have become the mainstream bridge type for large and medium spans. The connected tower cable-stayed bridge is different from traditional single span cable-stayed bridges, integrating the characteristics of split frame structure and connected tower design, and has better structural mechanical properties. However, the complex cable layout, multi tower structure, and segmented bridge deck of the cable-stayed bridge with connected towers result in essential differences in its mechanical behavior compared to traditional bridges. Therefore, studying the dynamic response of cable-stayed bridge cables when vehicle loads pass through the bridge is of great practical significance. This article comprehensively considers two factors: vehicle weight and vehicle speed, and introduces the parameter of dynamic amplification factor. The numerical simulation method is used to analyze the dynamic response of the cable when a vehicle load passes through a cable-stayed bridge with a connected tower. In order to provide key data support and important references for the construction and operation monitoring of cable-stayed bridges with connected towers.

Quantitative study on dynamic response of buried natural gas pipeline under vehicle load

With the intersections of buried pipelines and highways increasing, the pipelines are under the threat of heavy loads. In this article, a full-section dynamic stress testing system is designed and constructed. A transient vehicle–road-soil-pipeline numerical model based on ADAMS and ABAQUS is established. On this basis, the influence of the vehicle rolling position on the stress distribution is analyzed. The influence of seven important factors, such as vehicle load and pipeline internal pressure, on the maximum stress of the pipeline is investigated. It is found that the maximum stress point always appears in the 12o'clock of the pipeline and the vehicle load most significantly affects the maximum stress of the pipeline. Furthermore, based on the simulation results and the GA-BP neural network, a maximum stress prediction model for the buried pipeline is established. The prediction model shows a high accuracy with a maximum relative error of 11.1%. Through this prediction model, an ultimate bearing capacity method for buried pipelines under vehicle loads is established and applied in three in-service buried pipelines.

ПЕРСПЕКТИВНІ РОЗРАХУНКОВІ НАВАНТАЖЕННЯ ДЛЯ МОСТІВ НА АВТОМОБІЛЬНИХ ДОРОГАХ

Introduction. The article deals with the analysis and coverage of statistical data related to the development and application of temporary loads and the relevant norms for the design of bridge structures. Problem Statement. Over the past 5-7 years, the highway bridges of Ukraine have undergone significant changes in the intensity and composition of traffic flows. At the same time, the load, which is established by the current regulations, does not take into account the tendency to increase the weight of cars. At the same time, a significant part of the building's life cycle takes place under conditions of overload, which leads to premature destruction and overspending on repairs. In this regard, it is not always possible to unambiguously assign the type of calculated load, which can cause significant errors in the assessment of the strength and serviceability of bridge structures. Materials and methods. Comparison of estimated vehicle loads of models H-10, H-13, H-18, NK-80, Н-30, NK-100, NG-60, A11, A15, AB-51, AB-74, AB-151 (from 1931 to 2023) with the LM1 model of Eurocode EN 1991-2 shows that there is some balance between the two. The estimated load according to Ukrainian standards is intermediate between the standards of Europe and the USA. However, the level of characteristic loads is almost twice as low as in the Eurocode. This is a significant shortcoming of state building regulations, as it reduces the level of reliability of calculations.

Retraction Note: Virtual reality research on vibration characteristics of long-span bridges with considering vehicle and wind loads based on neural networks and finite element method

Retraction Note: Virtual reality research on vibration characteristics of long-span bridges with considering vehicle and wind loads based on neural networks and finite element method

A Review on Design and Fabrication of Mechanically Operated Gate System

Entryways have become increasingly common in private settings, serving as entrances to enclosed spaces or gaps in fences to regulate passage. Nowadays, many entry gates are operated by automated systems, boasting various advanced features. The demand for automated or mechanically operated doors has been steadily rising. The described automatic gate system is designed to automate entrances to parking lots of residential properties, businesses, and public car parks. It utilizes a mechanically controlled mechanism comprising a base plate connected to an L-shaped rod, which is in turn attached to a pulley fixed on the gate's main body. This setup enables the gate to be raised by spinning the pulley, and it gradually descends with the assistance of a car boot shock absorber when the vehicle's load is removed. This system ensures continuous gate opening and closing without the need for an external power source. While operating the gate manually reduces the burden on individuals, it is designed to function automatically through mechanical movements without requiring external control. Keywords: Mechanical Operated Gate, Pulley, Base plate, Boot shock

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

Minimizing the impact of electric vehicle charging station with distributed generation and distribution static synchronous compensator using PSR index and spotted hyena optimizer algorithm on the radial distribution system

This research investigates the impact of electric vehicle charging station on power loss, voltage stability, and reliability within radial distribution systems. With the escalating use of electric vehicles, understanding these effects is crucial for ensuring the stability and efficiency of radial distribution systems infrastructure. The study formulates the electric vehicle charging station allocation problem as a multi-objective function, integrating power loss, voltage stability, and reliability metrics expressed as the PSR index. Using the IEEE 69-bus system as a test system, the research explores the ramifications of electric vehicle charging station integration and proposes strategies to mitigate their effects. An innovative combination of distributed generation and distribution static compensator are employed to alleviate the impact of electric vehicle charging station on radial distribution systems. A recent optimization methodology called spotted hyena optimizer algorithm is introduced to determine optimal electric vehicle charging station locations, with results compared to other existing algorithms. The study assesses potential power losses induced by additional electric vehicle loads, explores strategies to optimize charging rates for minimizing resistive losses, and evaluates voltage stability and reliability implications. The important results include objective function values obtained using various algorithms: Spotted hyena optimizer algorithm (0.7683), cuckoo search algorithm (0.7709), bat algorithm (0.8035), and African vultures optimization algorithm (0.7856). The findings demonstrate that the proposed algorithm outperforms other algorithms in minimizing the objective function value, indicating its efficacy in optimizing electric vehicle charging station placement within radial distribution systems. In conclusion, this research contributes valuable insights into the challenges associated with integrating electric vehicle charging station into radial distribution systems and provides innovative solutions for optimizing electric vehicle charging station placement, thereby advancing the understanding and management of distribution systems in the context of electric vehicle adoption.

Dynamic serviceability and safety reliability analysis of aging PC girder bridges with non-prestressed reinforcement considering concrete shrinkage, creep and stochastic vehicle load flows

Prestressed concrete (PC) bridges’ service performance displays time-dependency due to several factors including concrete shrinkage, creep, resistance degradation, and stochastic vehicle load flows. These non-stationary vehicle load flows, combined with concrete shrinkage, creep, and resistance degradation, result in sequential alterations of internal forces and deformations in simply supported PC girder bridges, ultimately influencing their dynamic reliability. This study proposes a robust methodology that considers the impact of traffic loading, shrinkage, and creep to analyze the dynamic reliability of simply supported PC girder bridges through the development of a load probability model. A stochastic model for truck loads is generated using motion measurement data, which can be used to calculate bridge internal forces and subsequent quantitative analysis of structural deformation due to shrinkage and creep. Subsequently, the formula for the first exceedance probability under resistance degradation is presented and validated through Monte Carlo simulation. Reliability analysis is conducted for both serviceability and load carrying capacity limit states to assess the impact of shrinkage, creep, and increased mean vehicle load on structural reliability. The proposed method offers the theoretical foundation and practical guidance for assessing the structural health status of in-service bridges, thereby providing valuable insights into their long-term performance and maintenance requirements.

Optimization research on short-term load forecasting method for electric vehicles based on SSA-SVM

In order to improve the accuracy of short-term electric vehicle load forecasting, a combined forecasting model based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is constructed. Using SVM model to predict electric vehicle load, accelerating the convergence speed of the prediction model through sparrow search algorithm, conducting global optimization in the solution space, expressing the impact of calculated parameters on the model through fitness, searching for the optimal model parameter data, and improving the overall accuracy of the prediction model. Through simulation experiments, comparing the prediction results of SVM models, and SSA-SVM shows better performance in electric vehicle load prediction.

Fatigue performance of stud connectors in joints between top slabs and steel girders with corrugated webs subjected to transverse bending moments

Steel-concrete composite box girders with corrugated steel webs (CSWs) are broadly used in bridge engineering, in which the joints between top RC slabs and steel girders with CSWs are usually subjected to transverse bending moments caused by off-center vehicle loads. Stud connectors arranged in the joints usually bear repeated pull-out forces in this case and are prone to fatigue failure. Therefore, two reduced scale joints between top slabs and weathering steel girders with CSWs were designed and tested, on which variable amplitude fatigue loads were applied to investigate the fatigue performance of studs incorporated in the joints. The two joints presented similar fatigue behavior, i.e., cracks of not more than 0.26 mm in width appeared on the surface of the top slab during the fatigue loading, and the separation of the top RC slabs and the top girder flanges was observed; meanwhile, minute cracks developed along the welds connecting the CSW with the bottom girder flange, and the stiffness of the joints gradually decreased due to the accumulation of fatigue damage. The two joints underwent 2,650,000 and 2,103,600 load cycles respectively before fatigue failure occurred. After partially demolishing the RC slabs, several welded head studs in the joints were observed fractured at the roots and detached from the top girder flange. Whereafter, finite element (FE) models referring to the test joints were developed and analyzed, and the effect of the wave height (WH) of the CSW and the transverse row spacing (TRS) of studs on the distribution of pull-out forces among studs was investigated. It showed that the pull-out force bore by each stud in the joint was distributed unevenly even in the same row. Then, a modification factor α was introduced to revise the pull-out forces of studs calculated in the theoretical method. An equation to determine the factor was fitted, considering the effect of the WH of the CSW and the TRS of studs. Finally, the detail category for studs bearing pull-out forces in the joints was discussed based on the experimental results and the collected test data, and it was preliminarily recommended studs subjected to pull-out forces can be classified into detail category 45 specified by Eurocode 3 in fatigue design.

An accurate and low-cost vehicle-induced deflection prediction framework for long-span bridges using deep learning and monitoring data

Accurate vehicle-induced deflection prediction is critical to bridge structural management and maintenance. However, most existing studies use finite element models and weigh-in-motion (WIM) system to predict the deflection of bridges, the cost of building finite element models is expensive. An accurate and low-cost vehicle-induced deflection prediction framework is developed in this study. In this framework, the calculation method of the spatio-temporal distribution matrix of the vehicle loads is proposed. A fusion attention mechanism bidirectional long short-term memory (ATT-BiLSTM) model is used to predict vehicle-induced deflection. Taking a suspension bridge in China as an example, the accuracy of short- and long-term vehicle-induced deflection prediction is tested, and different deep learning models are used for comparison. The results show that the proposed framework can predict short-term vehicle-induced deflection with a mean absolute error (MAE) of less than 2 mm, which can also accurately predict the extrema of vehicle-induced deflection within a certain time window. The proposed framework is independent of finite element models and lowers the cost of bridge deflection prediction.

Smart Vehicle Load Management with Arduino Sensors

In the context of road safety, vehicle overloading remains a persistent concern, posing a risk of accidents, infrastructure damage, and potential harm to human life. This project introduces an innovative solution for mitigating this issue through an Arduino-based smart vehicle load management The system incorporates load cell, Ultrasonic sensor, an LCD display, and a buzzer to effectively detect and prevent vehicle overloading, contributing to improved road safety. The smart vehicle load management with arduino sensors is designed to monitor the weight distribution on a vehicle using strategically placed load cells and detect variations in vehicle obstacle detection through Ultrasonic sensors. Upon detecting an overload condition, the system promptly triggers a response by stopping the motor, which prevents further loading, and immediately displays a warning on the LCD screen. Simultaneously, it activates an audible alert through the buzzer. This rapid reaction not only averts potential accidents resulting from overloading but also extends the vehicle's operational life by minimizing wear and tear. The Arduino-based Vehicle Overload Detection System represents a cost-effective and efficient approach to addressing the critical issue of vehicle overloading. Its monitoring capabilities, coupled with instant warnings and alerts, establish it as a valuable tool in enhancing road safety and reducing the risks associated with overloading.

An interconnected suspension with adjustable roll and pitch stiffness (IS-ARPS) to enhance anti-roll and anti-dive/squat characteristics

The suspension system is critical in maintaining vehicle stability in roll and pitch directions. Hydro-pneumatic interconnected suspension systems, in which the suspension struts are connected through hydraulic hoses, flow control valves, and accumulators, have great potential to further enhance anti-roll and anti-pitch characteristics. To address the existing designs’ shortcomings, this study introduces an interconnected suspension with adjustable roll and pitch stiffness (IS-ARPS). It not only eliminates the conventional anti-roll bars but also enables adjustment of roll stiffness depending on the driving and vehicle load conditions. In the pitch direction, the IS-ARPS provides an additional amount of pitch stiffness to reduce the dive/squat during braking/acceleration. The mathematical modeling of the IS-ARPS is also straightforward, requiring less computational cost and making it more practical for real-time implementations. Overall, the IS-ARPS system represents a significant advancement in the design of roll and pitch coupled interconnected suspensions, offering a more practical and versatile solution. The above features are examined through co-simulation between MATLAB/Simulink and ADAMS/Car.