Random Traffic Flow Research Articles

Experimental investigation on static damage of concrete bridge under shear loading based on acoustic emission

This article presents techniques used to observe the shear behaviour of modified Z-shaped concrete specimens, experimental results, and a method proposed to classify the shear cracking pattern of a concrete bridge based on acoustic emission (AE). Z-shaped specimens under shear were designed with steel plate reinforcement. A finite element (FE) model for this specimen was developed to validate the concrete crack propagation. AE parameters, including the peak amplitude, counts, cumulative energy, average frequency (AF) and rise angle (RA), were found to successfully correlate with the observed cracking process in the specimens. A cluster analysis based on the K-means algorithm was proposed to automatically classify the AE signal into tensile and shear clusters. An existing bridge underwent reinforcement and then testing under two test scenarios. AE signals were detected at the web of the bridge under heavy truck loads or full random traffic flows. Using the proposed crack classification showed that the signals from the bridge belonged to shear crack clusters characterized by lower AE energy. Thus, this work developed an AE method to classify cracks in concrete shear tests and extended the method to classify the shear cracks in a bridge to provide an early warning of future failure.

Intelligent Simulation Method of Bridge Traffic Flow Load Combining Machine Vision and Weigh-in-Motion Monitoring

Random traffic flow load (TFL) simulation is an important analysis method for bridge design and safety assessment, and accurate TFL modelling is a prerequisite for high-quality simulation. The existing TFL modelling methods almost all rely on the load data monitored by the weigh-in-motion system (WIM system). However, the WIM system has natural defects such as unsatisfactory measurement accuracy at low speed and the inability to measure vehicle lengths and transverse positions in the lane, limiting the improvement of TFL simulation accuracy. Regarding this, a TFL monitoring system that integrates the functions of machine vision and WIM system is developed in this paper. In this system, a deep learning method is applied, for the accurate detection of vehicles and wheels in the video, and the extraction of key parameters for TFL modelling based on detection results. According to the long-term monitoring value, statistical distributions of key parameters are determined, and then an intelligent TFL model is derived from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intelligent Driver Model</i> (IDM), considering the car-following behavior of vehicles. Correspondingly, this paper further suggests a TFL simulation method and achieves an accurate TFL simulation. A cable-stayed bridge is taken as an example to verify the feasibility of the method. The results show that, compared to the modelling and simulation methods that only rely on the WIM system, the proposed method not only reduces the measurement error of vehicle dimensions by nearly 4 times, but also performs higher resolution in time measurement. The proposed method effectively overcomes the shortcomings of existing schemes and has good application potential in engineering.

Towards dynamic property control of bridge spans

Regulation of the dynamic properties of bridge spans is a priority field of this research, which solves the problem of increasing the obsolescence and physical periods of bridge structures manifested both at the design stage of the load redistribution in the load bearing and during long-term operation.Over the past 40 years, technical bridge diagnostics has shown that the durability and safe long-term operation can be ensured by the improved calculations, operation and stress and strain control under the excess and over-calculated live loads.The aim of this work is to control the dynamic deformation and amplitude-frequency characteristics of bridge spans under harmonic random (non-stationary) oscillations of the span-vehicle system due to changes in the energy and stress state of the structure. The dynamic behavior of the span-vehicle system is based on the control for the amplitude-frequency characteristics of random oscillations by averaged values, the required spectral density being provided.The use of dynamic dampers for the system element control and the rigidity of junctions provide antiphase oscillations of the bridge span elements such as beams and decks, that leads to the unaccounted inertial forces.Another important element of the joint work imbalance of the bridge span elements during the dynamic load, are various defects, both in the deck design and load-bearing elements. It is assumed that the deck is a transfer layer (element) of vibrations induced by a vehicle in the beams. It is shown that the control for the dynamic properties is required in the case of a coincidence between the vehicle and beam stiffness and mass at the center of the system rigidity.The attention is paid to the conditions and dependencies between the dynamic load parameters and the stress-strain state of the bridge beams at the elastic and elastoplastic stages, with respect to the additional inertia of the system. This approach is the pilot in the Russian and foreign bridge construction in terms of experimental studies and testing of bridges for continuous random traffic.The dynamic testing of bridge spans for random traffic flow contributes to the creation of vibration diagnostic express laboratories necessary for the operation and maintenance of bridges.

Dynamic response analysis of cable-stayed bridge under random traffic flow and fleet

The vehicle-bridge coupling vibration (VBCV) theory is being applied in the safety evaluation of existing bridges, such as cable-stayed bridge. In order to study the dynamic performance and vibration response of urban long-span cable-stayed bridges under traffic flow, and provide reference for the design, construction and safety assessment of existing bridges, the urban cable-stayed bridge with single tower and double cable in service was taken as the research object. The dynamic response of bridges under vehicles with different number, distance, speed and weight was analyzed. And the VBCV of bridge under different vehicle density and speed was discussed. The traffic flow on the bridge was simulated by the cellular automaton (CA) model, a half car model with four degrees of freedom was established, and the bridge models were established by the ANSYS software. According to the displacement coordination and mechanical balance conditions, the two models were connected, and were solved by MATLAB software. The dynamic response of the vehicle-bridge system under the vehicle fleet and random traffic flow was investigated. The research results showed that the vertical displacement (VD) of the main span increased with the number of vehicles, conversely, the vertical vibration acceleration (VVA) decreased. As driving distance increased, the VD and VVA of main span decreased. The VD of main span was not sensitive to the vehicle speed, but the VVA increased with the vehicle speed. The VD and VVA of the main span increased with the vehicle weight, and the VD of main span was proportional to the traffic density. As the traffic density increased, the VVA increased first, then decreased.

A bridge weigh-in-motion method of motorway bridges considering random traffic flow based on long-gauge fibre Bragg grating sensors

Accurate traffic conditions and their development regularity can help in the management of future traffic demand and bridge design. This study proposes a bridge weigh-in-motion method for identifying the vehicle parameters of motorway bridges considering random traffic flow and capitalizes on the advantages of long-gauge fibre Bragg grating sensors. A bridge-vehicle coupling system is numerically simulated, a 1:10 indoor experiment is used to verify the proposed method, and the vehicle parameters (e.g., traffic lane, vehicle spacing, vehicle axle weight, and speed) are identified with high accuracy. The effects of the road surface conditions, vehicle numbers, lateral positions and accelerations on the proposed method are investigated, and only find that the lateral positions are obviously related while and others have almost no influence on the method. In addition, the accuracy in axle number and weight identification is not high when the gauge length is smaller than vehicle wheelbases.

Impact of rigid central clamps on longitudinal deformation of long-span suspension bridges under vehicle excitations

The presence of rigid central clamps (RCCs) minimises the damage potential of short suspenders and expansion joints in in-service long-span suspension bridges. The study presented in this paper evaluated the impact of the presence of RCCs on the longitudinal deformation of a long-span suspension bridge under live load with vehicle braking and random traffic flow excitations. Two finite element (FE) models of the bridge were developed – one with RCCs and the other with a traditional suspender (TS) at mid-span. The impact of RCCs and TS on the structural system response was evaluated by comparing the dynamic characteristics of the models. The exceeding probabilities of longitudinal deformation under different traffic flow levels during the design reference period were obtained. The simulation results show that the RCCs are capable of reducing the longitudinal deformation by 35% to 45%. The unfavourable exceeding probabilities of longitudinal deformation over the design threshold decreased from 1.0 to 0.0002, showing that the RCCs could be an effective measure to enhance the service life of the shortest suspender and expansion joints of long-span suspension bridges.

Dynamic Response of Multitower Suspension Bridge Deck Pavement under Random Vehicle Load

Recently, the multitower suspension bridge has been widely used in long‐span bridge construction. However, the dynamic response of the deck and pavement system of the multitower suspension bridge under random vehicle load is still not clear, which is of great significance to steel‐bridge deck pavement (SBDP) design and construction. To reveal the mechanical mechanism of the steel‐bridge deck pavement of the multitower suspension bridge under traffic load, this paper analyzed the mechanical response of the pavement based on case study through the multiscale numerical approach and experimental program. Firstly, considering the full‐bridge effect of the multitower suspension bridge, the finite element model (FEM) of the SBDP composite structure was established to obtain key girder segments. Secondly, the influences of pavement layer, bending moment and torque, random traffic flow, and bridge structure on the stress of the girder segment were analyzed. Thirdly, the mechanical response of the pavement layer to the orthotropic plate under random vehicle load was studied. Finally, a full‐scale model of the experimental program was established to verify the numerical results. Results show that (1) the pavement layer reduced the stress of the steel‐box girder roof by about 10%. In the case of adverse bending moment and torque, the longitudinal and transverse stresses of the pavement layer were mainly concentrated in the stress concentration area near the suspender. Under the action of the random vehicle flow, the stress response of the pavement layer was increased by 40% compared with that under standard load. (2) Three‐tower and two‐span bridge structures have a great influence on the vertical deformation of the pavement layer under the action of vehicle load. Thus, the pavement material needs to have great deformation capacity. (3) The full‐bridge effect has a significant influence on the longitudinal stress of the local orthotropic plate, but a small influence on the transverse stress. (4) There is a good correlation between the experimental measurement results of the full‐size model and that of the numerical model. The research results can provide guidance for SBDP design and construction of the multitower suspension bridge.

Evaluation of dynamic responses of bridges considering traffic flow and surface roughness

The variations in the vehicle types, speeds, and passing order in the traffic flow and roughness conditions cause the dynamic effects of live loads on the bridges. In this study, a numerical calculation scheme is proposed to evaluate the bridge dynamic response under random traffic flow, considering the surface roughness condition. Traffic vehicles with a random passing order are constructed as the time history of the transient force functions assigned at each discretised deck node of the numerical model. The random effects of the vehicle types, speeds, and surface roughness are represented by the amplitude levels of the transient force functions. The validity of the calculation scheme is verified by applying it to an existing steel box-girder bridge using dynamic monitoring data, traffic data, and a finite element model. The behaviour of the calculated root mean square (RMS) of the accelerations agrees with that of the measurement data, particularly in the traffic situation with a high truck ratio. Moreover, a parametric study is conducted to investigate the effects of the traffic flow characteristics and surface roughness conditions on the bridge dynamic responses. The results demonstrate that the RMS of acceleration depends on the truck ratio and traffic flow speed; however, the maximum acceleration may be influenced by the vehicle speed and passing order, as well as the surface roughness condition. It is concluded that the calculation scheme is applicable for evaluating the dynamic responses of existing bridges with the given dynamic monitoring data, traffic flow, and surface roughness conditions.

Augmented extended Kalman filter with cooperative Bayesian filtering and multi‐models fusion for precise vehicle localisations

Self-localisation is vital for autonomous vehicles. In this study, the authors present an augmented extended Kalman filter (AEKF) framework for intelligent vehicle localisation applications. Compared to the previous approach, the proposed AEKF is enhanced through a model fusion, which incorporates a constant velocity model, constant acceleration model, constant turn rate and velocity, and constant turn rate and acceleration model by using the Takagi–Sugeno fuzzy inference technique, where the typical prediction procedure in the extended Kalman filter is modified by a fusion of those various motion models for the state estimation. Furthermore, they proposed a flexible cooperative Bayesian filter to incorporate the data from nearby-vehicles’ position and lateral distance from the host vehicle to the lane lines, to improve the raw global positioning system (GPS) performance under multi-sensor observation environments. They conduct simulation experiments under vividly, near-realistic scenarios with random traffic-flows to show the superiorities of the proposed framework when compared with the consumer-grade GPS implementation. The results show that the obtained positioning enhancement can significantly reduce the positioning error from the original larger than 5 m to the sub-meter level under various scenarios.

Comprehensive benefit analysis of reinforced concrete bridge under different axle load limits

Limiting the vehicle load crossing the bridge can improve the safety of the bridge, but the transportation efficiency might be reduced. In order to reasonably evaluate the comprehensive impact of vehicle load limit on bridges, a comprehensive benefit evaluation factor K was defined to analyze the comprehensive benefits of highway bridge transportation under different axle load limits. Firstly, based on WIM data, the random distribution function of total vehicle weight and axle load was fitted, and the loss ratio of transportation benefit before and after limiting the axle load was defined and calculated based on the principle of invariable total vehicle weight. Combined with the random distribution function of the total weight and axle load, Monte-Carlo method was used to simulate the random traffic flow under different load limit levels and calculate the time-varying reliability of typical bridges under different load limit levels. Finally, the evaluation factor K was introduced to evaluate the influence of axle load limit on the comprehensive benefit of highway reinforced cocrete bridges, and reasonable axle load limit suggestions were put forward.

Modeling Traffic Function Reliability of Signalized Intersections with Control Delay

The performance of intersections has been considered a key factor in measuring the efficiency of urban road systems. In this paper, a reliability model for a two‐phase signalized intersection is proposed on the basis of presenting a concept of traffic function reliability (TFR). First, classic cumulative curves are created to derive delay formulas. Then, a model for traffic function reliability is proposed based on the quantitative relationship between the random traffic flows, signal timing, and queue lengths. Finally, the delay threshold of the intersection is determined by referring to the level of service. A numerical simulation has been created to clarify the proposed mechanism of TFR. The results show that the saturation and the green time ratio have a dramatical influence on TFR. Under different saturation levels, the sensitivity of TFR to changes in green time ratio gradually weakened. When the green signal ratio increases above a certain value, TFR remains nearly constant. A microscopic simulation verified the applicability of the proposed model. The results show that the accuracy of the model is close to 90% in the case of low saturation. This method provides road authorities useful insights to understand travel time variability.

Fatigue behavior of orthotropic bridge decks with two types of cutout geometry based on field monitoring and FEM analysis

This paper provides comparative results on stress behavior and fatigue performance of two types of cutout geometry on orthotropic bridge decks based on simultaneous field monitoring and finite-element method (FEM) analysis. The two types of cutout are incorporated in two closely spaced diaphragms with distance of 6 m and at the same transverse location on a real bridge, allowing simultaneous stress measurement under random traffic flows. The research was conducted on the Pingsheng Bridge in Southern China, a self-anchored suspension bridge with a main span of 350 m and a steel box girder with orthotropic steel bridge decks (OSBD). Results of the study reveal that the two types of cutout geometry present high stress response and severe stress concentration at the floorbeam (FB) cutout, and that the excessively large stress at the original FB cutout contributes greatly to the early cracks of this detail. Compared to the original cutout geometry, the new cutout geometry with large radius increases the stress level at the FB cutout, resulting in a further low fatigue life at this detail. Meanwhile, the new cutout geometry also deteriorates the fatigue resistance at the rib-to-floorbeam (RF) weld connection, particularly at the detail of rib wall at cutout, where the stress level is significantly increased and its fatigue life becomes substantially lower than the design life of 100 years. In addition, the new cutout geometry only slightly improves the out-of-plane distortion under direct wheel loading, and the in-plane stress still dominates the total stress at the FB cutout. Since the new cutout geometry weakens the FB web, the total stress at FB cutout will increase due to the increased in-plane stress. The research also finds that if the free edge of cutout did not satisfy the fabrication requirement per the AASHTO LRFD, category B is suggested for fatigue evaluation at the FB cutout, which is confirmed by the observed fatigue life of the original FB cutout on the real bridge. It is concluded that the new cutout geometry lowers the fatigue resistance both at the FB cutout and at the RF weld connection, hence it will no longer be recommended to retrofit the present bridge in the future.

Multi-parameter Dynamic Traffic Flow Simulation and Vehicle Load Effect Analysis based on Probability and Random Theory

In order to evaluate the vehicle load adaptability of existing highway bridge at the operational stage, and provide a reference for the strategy of reinforcement and maintenance, a new model of multi-parameter traffic load flow simulation and vehicle load effect (VLE) analysis is proposed. In this paper, static traffic flow data was collected by weight in motion (WIM) system, and the characteristics of static traffic flow, such as gross vehicle weight (GVW), vehicle gap distance (VGD), wheelbase and the proportion of axle weight distribution (PAWD), was analyzed in different lanes. The program run by MATLAB was used to conduct the Monte-Carlo method on all kinds of traffic data collected and to generate the multi-parameter dynamic random traffic flow (DRTF) data. By loading DRTF data onto the influence line of the sample bridges (simply supported integral bridges of 20 m to 40 m), the maximum VLE under DRTF could be calculated and compared with the static VLE of Chinese current design code. The results showed that the fitting of GVW distribution should be used a skewed distribution or a kurtosis distribution. And under current daily traffic volume (DTV), the service condition of highway bridges can meet the requirement of traffic load. However, in the driveway of some heavy trucks, the VLE is greater than the value suggested in the current design code, which may cause partial damage to the bridge.

The Design of Control System for Gearless Synchronous Drum-Motor of the Underground Mine Belt Conveyor

Regarding the current tendency of replacing the geared electric drives with gearless drum motor and substitution of the obsolete induction motors with energy-efficient synchronous motors, the authors consider the difficulties of the high-power permanent magnet synchronous motor and present its model with parameters corresponding to the real belt conveyor electric drive parameters. This paper describes the questions of the vector control system simulation for the synchronous gearless drum-motor of an underground belt conveyor. The control of permanent magnet synchronous motor is realized having regard to rotor angle and random traffic flow. The mechanical system of an underground belt conveyor is represented by the multimass system with the viscoelastic bar. As a conclusion, the article presents the results of the direct torque control system simulation in the speed adjustment mode.

Markov probabilistic decision making of self-driving cars in highway with random traffic flow: a simulation study

Purpose Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations. Design/methodology/approach In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment. Findings Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy. Originality/value This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

Analysis and prediction of operating vehicle load effects on Highway bridges under the weight charge policy

Under the weight charge policy, the weigh in motion data at a toll station on the Jing-Zhu Expressway were collected. The statistic analysis of vehicle load data was carried out. For calculating the operating vehicle load effects on bridges, by Monte Carlo method used to generate random traffic flow and influence line loading method, the maximum bending moment effect of simple supported beams were obtained. The extreme value I distribution and normal distribution were used to simulate the distribution of the maximum bending moment effect. By the extrapolation of Rice formula and the extreme value I distribution, the predicted values of the maximum load effects were obtained. By comparing with vehicle load effect according to current specification, some references were provided for the management of the operating vehicles and the revision of the bridge specifications.

Numerical Investigation of the Dynamic Responses of Long-Span Bridges With Consideration of the Random Traffic Flow Based on the Intelligent ACO-BPNN Model

With emphasis on long-span bridges, the dynamic responses of bridges without considering random traffic flows were found to be different from actual situations. The introduction of a random traffic flow model provides a new approach for the random analysis of bridge structure responses under vehicle loads. In this paper, the finite element and intelligent ant colony optimization-back propagation neural network (ACO-BPNN) models were used to study the dynamic responses of long-span bridges. The computational model was also validated by an experimental test. To confirm the validity of the proposed ACO-BPNN model after parameter selection, it was compared with the traditional back propagation neural network (BPNN) model and the genetic algorithm-back propagation neural network (GA-BPNN) model. BPNN, GA-BPNN, and ACO-BPNN adopt the same network topology structure to predict the dynamic responses of the long-span bridge. When the ACO-BPNN model conducted the iteration to the 130th generation, a training error of 0.009 was found to be smaller than the set critical error. In this manner, the computational accuracy was increased, and the optimized time was reduced. In addition, only 0.4 hours were spent in using the proposed ACO-BPNN model to predict the dynamic response of the long-span bridge. In the case of the same computer performance, it took 4.5 h to use the finite element model to predict the dynamic response of the long-span bridge. The advantage of the proposed ACO-BPNN model in predicting the performance of large-scale complex structures such as long-span bridges was clearly found.

Numerical computation for vibration characteristics of long-span bridges with considering vehicle-wind coupling excitations based on finite element and neural network models

CA (Cellular Automaton) model was applied to the simulation of random traffic flow to develop a model considering the randomness of traffic flow and apply it to wind-vehicle-bridge coupling vibration. Finite element and neural network models were adopted respectively to numerically compute the vibration characteristics of bridges under wind and vehicle loads, verify the correctness of model. Subspace iteration method was used for the modal analysis of bridges. Natural frequencies of the top 8 orders were 0.21 Hz, 0.27 Hz, 0.36 Hz, 0.45 Hz, 0.56 Hz, 0.66 Hz, 0.87 Hz and 1.02 Hz respectively. The vibration frequency of the long-span bridge was consistent with the vibration characteristics of large-scale complex structures. Natural modes mainly reflected the torsion and bending of main beam and the swinging vibration of side and main towers. Fluctuation wind time-history presented periodic characteristics. The maximum and minimum values of fluctuation wind were about 20 m/s and –20 m/s respectively. The target and simulation values of power spectral density of wind speed were basically the same in change trend, which indicated that the fluctuation wind time-history computed in this paper was reliable. The model of dense traffic flow based on CA more truly described the running status like accelerating, decelerating and changing lanes of vehicles on the bridge, also contained the density information of vehicles and more truly reflected traffic characteristics. Vibration accelerations of the long-span bridge were symmetrically distributed. Vibration acceleration of central position in the left main span was the largest and near 50 cm/s2; vibration acceleration on the main tower was the smallest. The curve of vibration displacement with considering wind loads presented some fluctuations, while the vibration displacement of bridges without considering wind loads was very smooth. In addition, the amplitude of vibration displacement without considering wind loads moved laterally towards the left compared with that with considering wind loads. Therefore, wind loads must be considered when the vibration characteristics of the long-span bridge were computed. Otherwise, the accuracy of computational results would be reduced. It only took 0.5 hours to use neural network to predict the vibration acceleration of the long-span bridge. In the case of the same computer performance, it took 5 hours to use finite element model to predict the vibration acceleration of the long-span bridge. The advantage of neural network model in predicting the performance of large-scale complex structures like a long-span bridge could be obviously found. In the future, we will consider using neural network model to systematically study and optimize the long-span bridge.

Effect of live load on simply supported bridges under a random traffic flow based on weigh-in-motion data

Weight, speed, axle space, and following distance of the vehicles are important factors that determine the live load for simply supported beam bridges. In this study, the effect of live load on simply supported bridges was investigated by considering these parameters. The parameters namely weight, speed, longitudinal following space, and vehicle occupancy ratio on different driving lanes are considered using different numerical models. Then, the random traffic flow is simulated using Monte Carlo method based on Nan-xi Yangzi River Bridge’s weigh-in-motion system data. Finite element models are established for six types of simply supported beam bridges with the span ranging from 6 to 40 m and transient analysis is simulated under the traffic loading. Subsequently, the maximum value distribution of live load for lifetime is obtained for the collected data by the method of extrapolation. This study shows that the mean value of the live load for the simply supported beam bridges under a random traffic flow is lower than the permissible design load as per American Association of State Highway and Transportation Officials; however, it is higher than the permissible design load as per BS5400, and it is 0.82 times the Highway-I standard load.