Axle Weight Research Articles

Enhancement of stiffness of truck cabin against roof loading as per AIS 029 using advanced finite element approach

This research work focuses on the safety of occupants during a cabin crash of a commercial vehicle, which has always played a crucial role in sustaining Indian economy. Although the government had implemented some norms to ensure the safety of occupant for commercial vehicles. The economic wheels of India (LDT) are endangered in frontal collisions at high speed as they are generally used for transportation of goods on the highway. There is always a question mark on the safety of drivers during frontal collisions. We can explore for better design in terms of improving safety perspective Due to its payloads the energy during collision is high. As safety regulations were imposed OEMs of the commercial vehicle needed to focus on the safety factors including the occupant safety parameters like AIS029. The regulation is generally regarding the cabin strength and energy absorption at the time of frontal impact scenarios with regards to respective GVW and axle weight to ensure minimum damage to the occupant. The injury risk in specific frontal collisions can be reduced by applying crash technology within the roof structure of the coach. Simulation according to AIS029 roof strength was successfully carried out. Detailed model with proper material properties was used for the CAE simulation.. The chances of modifications were observed and to increase the protection of the occupant certain modifications were made in the design of roof of the cabin .The new stiffener design was added and it was found to display better crash protection functionality than the baseline roof. In this study, we have been evaluated and present a correlation between CAE and physical test for roof strength test. We can explore for better design in terms of improving safety perspectives.

Dynamic Response and Long-Term Settlement of a Compacted Loess Embankment under Moving Train Loading

There have been many railway construction projects in the loess region of China. Embankment is typically required for rail projects in these regions, since the railway basement is restricted by longitudinal slope requirements. However, there has been little study of the dynamic response of compacted loess embankment under moving train loading. The 2.5D finite element method was adopted to investigate this process and characterize the effects of train speed, height of embankment, and axle weight on the dynamic behavior of subgrade. A rectangular core zone of subgrade was determined, and a prediction model was established to evaluate the long-term settlement of embankment generated by moving train loading. The results showed that embankment height had negligible influence on the variation of dynamic stress. Decays of stress amplitude in both the vertical and horizontal directions slowed with increased train speed. Additionally, the dynamic stress increased linearly with the increase in axle weight due to the linear stress-strain relationship of soil. In the practical speed range (≤ 100 m/s), the dynamic influence depth increased with increasing speed in a range of 3–6 m. A core zone depth of 6 m reflects the effects of moving train loading, with a width of 4 m. For practical conditions (v ≤ 100 m/s), only slight settlement of embankment was observed (≤ 6 mm). However, it is difficult to achieve the same physical parameters used in the experiments (moisture content and compaction degree) in engineering practice. Further work should explore long-term dynamic settlement with relation to the degree of compaction of the embankment.

Data-driven analytical load rating method of bridges using integrated bridge structural response and weigh-in-motion truck data

Load rating is a widely used approach for evaluating the load-carrying capacity of bridges in an effort to ensure safe bridge operation under expected traffic loads. Load rating often relies on simplified analytical models including empirically derived model parameters that do not reflect bridge-specific information resulting in conservative ratings. To reduce this conservatism, this study proposes a novel data-driven framework that utilizes long-term bridge response data to extract bridge-specific model parameters that can be used within in the Load and Resistance Factor Rating (LRFR) process. The data-driven LRFR (DD-LRFR) framework is empowered by a cyber-physical system (CPS) architecture that uses Internet connectivity to integrate measured bridge responses with truck weights measured by a weigh-in-motion (WIM) station. The CPS architecture uses computer vision of camera images to confirm trucks observed at a WIM station are identical to those observed at a bridge. Bridge response and axle weight data are then used to extract probabilistic models of dynamic load allowances and unit influence lines. The DD-LRFR method is validated using a 20-mile (32.2-km) segment of the I-275 northbound highway in Michigan that is monitored continuously by the CPS architecture. Six girders associated with two bridges along I-275 are rated using the proposed DD-LRFR methodology with rating factors compared to those obtained using conventional and refined load rating methods. The DD-LRFR method yields inventory- and operational-level rating factors that are less conservative than those from the approximate LRFR method and comparable to those using finite element modeling of the bridge.

An investigation of bridge influence line identification using time-domain and frequency-domain methods

The method to obtain an accurate influence line (IL) from the direct measurement is an important research topic for structural condition assessment, model correction and bridge weigh-in-motion (BWIM) system. The two most common approaches used for the identification of IL are the time-domain (TD) method and the frequency-domain (FD) method. Despite having a similar mathematical framework, the TD and the FD methods are treated as two different methods by the researchers working on this field. This paper presents a detailed theoretical demonstration to show that the two methods discussed above are nothing but the same. The two methods were compared experimentally by using field measurement data on an existing steel girder bridge which were obtained by using three calibration trucks (CTs) with different axle weights and axle configurations. Although the ILs identified by the two methods were apparently different, but a theoretical insight into the frameworks revealed that the TD and FD methods are basically the same and a seeming difference between the two methods is due to the inherent assumptions involved in the discrete Fourier transform (DFT) such as the assumption of cyclic nature of analysis interval. Finally, a method to obtain an accurate influence line has been outlined.

Corrosion-fatigue life assessment of RC plate girder in heavy-haul railway under combined carbonation and train loads

Fatigue performance of RC plate girder was experimentally investigated. A corrosion-fatigue life assessment approach of railway RC girders under combined carbonation and train loads was proposed. The proposed approach was applied to an 8 m-span RC plate girder in an existing heavy-haul railway bridge, and the effects of annual freight volume, train axle weight, and carbonation environment level on corrosion-fatigue life were analyzed. Results indicate that annual freight volume, train axle weight, and carbonation environment level have a significant impact on corrosion-fatigue life, which can decrease the remaining corrosion-fatigue life by up to 48.71%, 91.11%, and 60.90%, respectively.

Calculation of Equivalent Axle Load Factor Based on Artificial Intelligence

In most road pavements design methods, a solution is required to transform the traffic spectrum to standard axle load with using equivalent axle load factor (EALF). The EALF depends on various parameters, but in existing design methods, only the axle type (single, tandem, and tridem) and pavement structure number were considered. Also, the EALF only determined for experimental axles and axle details (i.e., axle weight, length, pressure), wheel type (single or dual wheel) plus pavement properties were overlooked which may cause inaccuracy and unusable for the new axle. This paper presented a developed model based on Artificial Neural Network (ANN) for calculation of EALFs considering axle type, axle length, contact area, pavement structure number (SN), tire pressure, speed, and final serviceability. Backpropagation architecture was selected for the model for the EALF prediction based on fatigue criteria. Finally, among all reviewed ANN configuration, a network with 7-13-1 was selected for the optimum network.

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

AbstractTraffic information plays an important role in the design and management of civil transportation infrastructure. Bridge weigh‐in‐motion (BWIM) provides an effective tool for traffic information gathering by estimating vehicle parameters including its weight through bridge responses. Most existing BWIM algorithms rarely consider the epistemic uncertainty of vehicle weight in terms of the probabilistic distribution of estimated axle weights (AWs) of the vehicle. This paper proposes a novel methodology for probabilistic vehicle weight estimation using a physics‐constrained generative adversarial network (GAN). Generative models are introduced to describe the probabilistic distributions of estimated AWs and bridge responses. Physics constraints on the generative models are formulated and enforced by minimizing a physics‐based loss function. The generative models are then learned by training a physics‐constrained GAN using the observed bridge responses. Numerical study and field testing are conducted to demonstrate the proposed method using representative highway bridges and vehicles. The results show that the proposed method can successfully capture the uncertainty in the vehicle weight estimation and provide the probabilistic distributions of the estimated AWs for different vehicle types and loading conditions considered, which can enhance the application of BWIM for relevant tasks such as traffic data collection and truck overloading enforcement. Based on the results obtained from the numerical study and field testing, the maximum coefficient of variation obtained for the AWs and gross vehicle weight of the presented cases are 0.55 and 0.11, respectively.

Field test and semi-analytical simulation of unsaturated road subgrade in various water content subjected to a heavy duty truck

Field test is the most direct and essential way to evaluate subgrade dynamic responses, which is of great importance in designing and ensuring long-term subgrade performance. Many researchers have performed field tests considering the dynamic responses of subgrade. However, in the previous tests, the water content of the soil, which decides the mechanical properties of unsaturated soil, is rarely considered. In this paper, the dynamic responses of subgrade in different saturations are investigated by both field tests and analytical simulations. The vertical stress and acceleration tested in the field under different water contents, depths, truck speeds, and axle weights are presented. The result shows that as the water content in the subgrade rises, vertical stress variation is insignificant, while the amplitude of acceleration increases. According to the distribution of water content of the subgrade, a double-layer unsaturated soil model was proposed using the stiffness matrix method. The comparison between field test and simulation results shows that the agreement is good.

Bridge weigh-in-motion through bidirectional Recurrent Neural Network with long short-term memory and attention mechanism

In bridge weigh-in-motion (BWIM), dynamic bridge response is measured during traffic and used to identify overloaded vehicles. Most past studies of BWIM use mechanics-based algorithms to estimate axle weights. This research instead investigates deep learning, specifically the recurrent neural network (RNN), toward BWIM. In order to acquire the large data volume to train a RNN network that uses bridge response to estimate axle weights, a finite element bridge model is built through the commercial software package LS-DYNA. To mimic everyday traffic scenarios, tens of thousands of randomized vehicle formations are simulated, with different combinations of vehicle types, spacings, speeds, axle weights, axle distances, etc. Dynamic response from each of the randomized traffic scenarios is recorded for training the RNN. In this paper we propose a 3-stage Bidirectional RNN toward BWIM. Long short-term memory (LSTM) and attention mechanism are embedded in the BRNN to further improve the network performance. Additional test data indicates that the BRNN network achieves high accuracy in estimating axle weights, in comparison with a conventional moving force identification (MFI) method.

Bayesian Bridge Weigh-in-Motion and Uncertainty Estimation

Many researchers have developed bridge weigh-in-motion (BWIM) technology, mainly focusing on the representative value of the estimated axle weights. However, the estimation of the probabilistic distribution of axle weights is also important for understanding the ill conditioning of BWIM formulations and the uncertainty of estimation. Bayesian updating provides a coherent framework for assimilating data into models. Here, Bayesian bridge weigh-in-motion (BBWIM), which combines Bayesian updating and BWIM, is proposed. BBWIM can estimate not only the representative value of axle weights but also the uncertainty of the estimated value and the correlation among estimates. Uncertainties in estimated axle weight are quantitatively discussed with a simple two-axle problem. It is shown that the estimated weights of closely spaced axles have large uncertainty. BBWIM is applied to the measured data for an actual bridge. It is shown that additional information, in the form of a weak constraint on axle weight, namely, that closely spaced axles have similar weights, can reduce the uncertainty of estimated axle weights.

Static Lateral Stability of Tractor with Rear Wheel Ballast Weights: Comparison of ISO 16231-2 (2015) with Experimental Data Regarding Tyre Deformation

The paper presents a static lateral stability of a sub-compact tractor MT8-070 Mini in relation to a safe tractor operation. Axle weight distribution of the tractor was measured to calculate the vertical and longitudinal coordinates of a centre of gravity (COG). Experiments were aimed at the tractor equipped with no and four levels of rear wheel ballast weights (30.5, 61, 91.5 and 122 kg) at standard and extended overall width on tyres. A static overturning angle was calculated and experimentally measured when the tractor with right wheels touching the ground was in a state of unstable equilibrium. Comparing the experimental data with ISO 16231-2 (2015), the differences were 2.57%, 2.80%, 3.04%, 3.42% and 3.88% in the case of the standard overall width on tyres and 2.40%, 2.61%, 3.11%, 3.67% and 3.99% in the case of the extended overall width on tyres at 0, 30.5, 91.5 and 122 kg of the rear wheel ballast weight. Considering the vertical tyre deformation and the lateral shift of the tyre, the differences decreased to 0.95%, 1.11%, 1.29%, 1.85% and 1.42% (standard overall width on tyres) and 0.91%, 1.18%, 1.69%, 2.27% and 2.57% (extended overall width on tyres). The length of a rubber lug of a tyre contact patch did not change when the tractor was inclined at various ballast weights and did not affect the static overturning angle calculation according to ISO 16231-2 (2015). Results showed higher static overturning angle experimentally measured in comparison with calculated according to ISO 16231-2 (2015) due to the tyre deformation. Limiting the tractor operation on the basis of the static overturning angle calculated according to ISO 16231-2 (2015) avoids the tractor usage under dangerous operation conditions.

Axle Weights in combined Vehicle Routing and Container Loading Problems

Overloaded axles not only lead to increased erosion on the road surface, but also to an increased braking distance and more serious accidents due to higher impact energy. Therefore, the load on axles should be already considered during the planning phase and thus before loading the truck in order to prevent overloading. Hereby, a detailed 2D or 3D planning of the vehicle cargo space is required. We model the Axle Weight Constraint for trucks with and without trailers based on the Science of Statics and provide flexible formulas for different axle configurations of trucks. We include the Axle Weight Constraint into the combined Vehicle Routing and Container Loading Problem (“2L-CVRP” and “3L-CVRP”). A hybrid heuristic approach is used where an outer Adaptive Large Neighbourhood Search tackles the routing problem and an inner Deepest-Bottom-Left-Fill algorithm solves the packing problem. Moreover, to ensure feasibility, we show that the Axle Weight Constraint must be checked after each placement of an item. The impact of the Axle Weight Constraint is also evaluated.

Parameter tuning of a local search heuristic for a vehicle routing problem with loading constraints

A vehicle routing problem (VRP) with sequence-based pallet loading and axle weight constraints is introduced in the study. An Iterated Local Search (ILS) metaheuristic algorithm is used to solve the problem. Like any metaheuristic, a number of parameters need to be set before running the experiments. Parameter tuning is important because the value of the parameters may have a substantial impact on the efficacy of a heuristic algorithm. While traditionally, parameter values have been set manually using expertise and experimentation, recently several automated tuning methods have been proposed. The performance of the routing algorithm is mostly improved by using parameter tuning, but no single best tuning method for routing algorithms exists. The tuning method, Iterated F-race, is chosen because it seems to be a very robust method and it has been shown to perform well on the ILS metaheuristic and other metaheuristics. The research aims at developing an algorithm, which performs well over a wide range of network sizes.

BwimNet: A Novel Method for Identifying Moving Vehicles Utilizing a Modified Encoder-Decoder Architecture.

Traffic loading monitoring plays an important role in bridge structural health monitoring, which is helpful in overloading detection, transportation management, and safety evaluation of transportation infrastructures. Bridge weigh-in-motion (BWIM) is a method that treats traffic loading monitoring as an inverse problem, which identifies the traffic loads of the target bridge by analyzing its dynamic strain responses. To achieve accurate prediction of vehicle loads, the configuration of axles and vehicle velocity must be obtained in advance, which is conventionally acquired via additional axle-detecting sensors. However, problems arise from additional sensors such as fragile stability or expensive maintenance costs, which might plague the implementation of BWIM systems in practice. Although data-driven methods such as neural networks can estimate traffic loadings using only strain sensors, the weight data of vehicles crossing the bridge is difficult to obtain. In order to overcome these limitations, a modified encoder-decoder architecture grafted with signal-reconstruction layer is proposed in this paper to identify the properties of moving vehicles (i.e., velocity, wheelbase, and axle weight) using merely the bridge dynamic response. Encoder-decoder is an unsupervised method extracting higher features from original data. The numerical bridge model based on vehicle-bridge coupling vibration theory is established to illustrate the applicability of this new encoder-decoder method. The identification results demonstrate that the proposed approach can predict traffic loadings without using additional sensors and without requiring vehicle weight labels. Parametric studies also show that this new approach achieves better stability and reliability in identifying the properties of moving vehicles, even under the circumstances of large data pollution.

Investigation of the Fatigue Vehicle Load for a Long-span Steel Bridge Based on Nine Years of Measured Traffic Data

ABSTRACT This study conducts a rational investigation of nine years of traffic monitoring information concerning the Jiangyin Yangtze River Bridge within which the traffic flow, vehicle composition, vehicle load and axle-weight features are analyzed and discussed. Based on the annual analysis of traffic information, it is observed that compact cars weighing less than 30 kN account for 61% of the total traffic volume. According to the number of axles, wheelbase and axle groups, the vehicles in the traffic flow are classified into 11 representative types. Among them, two-axle vehicles form the major category, accounting for 80% of the total traffic flow. Statistical analysis of the gross vehicle weight and axle weight indicates that the gross vehicle weight follows a bimodal normal distribution, whereas the axle weight follows diverse distributions, including a normal distribution and an extreme distribution. In addition, by using equivalent damage theory, five equivalent vehicle models are established according to measurements of the axle weight and wheelbase. Finally, a six-axle fatigue-loaded vehicle model is proposed and discussed.

Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.

Research on the performance of heavy-haul rail breakage detection using track circuit

Abstract Rail breakage is one of the major safety risks in railway transportation. Because of the large axle weight, high density and large capacity of the heavy-haul railway, the damage to rail caused by heavy-haul train wheels will be more serious than that caused by ordinary passenger and cargo trains, resulting in a higher frequency of rail breakage. Taking the Daqin Railway Line as the research object, this paper analyses and discusses rail breakages occurring in interstation tracks and in-station tracks by establishing the ZPW-2000A track circuit calculation model considering the land leakage resistance between the rail line tracks; introduces the defining standards and measurement index of the broken rail coefficient to quantitatively analyse the influence of various influencing factors on the rail breakage inspection performance under the most unfavourable working conditions; and compares the model simulation data, the laboratory model data and the field test results to verify its effectiveness, so as to provide a reference and theoretical basis for the subsequent improvement and solution of the heavy-haul railway rail breakage problem.

Smart Graphite-Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing.

Smart multifunctional composites exhibit enhanced physical and mechanical properties and can provide structures with new capabilities. The authors have recently initiated a research program aimed at developing new strain-sensing pavement materials enabling roadway-integrated weigh-in motion (WIM) sensing. The goal is to achieve an accurate WIM for infrastructure monitoring at lower costs and with enhanced durability compared to off-the-shelf solutions. Previous work was devoted to formulating a signal processing algorithm for estimating the axle number and weights, along with the vehicle speed based on the outputs of a piezoresistive pavement material deployed within a bridge deck. This work proposes and characterizes a suitable low-cost and highly scalable cement-based composite with strain-sensing capabilities and sufficient sensitivity to meet WIM signal requirements. Graphite cement-based smart composites are presented, and their electromechanical properties are investigated in view of their application to WIM. These composites are engineered for scalability owing to the ease of dispersion of the graphite powder in the cement matrix, and can thus be used to build smart sections of road pavements. The research presented in this paper consists of electromechanical tests performed on samples of different amounts of graphite for the identification of the optimal mix in terms of signal sensitivity. An optimum inclusion level of 20% by weight of cement is obtained and selected for the fabrication of a plate of 30 × 15 × 5 cm3. Results from load identification tests conducted on the plate show that the proposed technology is capable of WIM.

Iterative linear optimization method for bridge weigh-in-motion systems using accelerometers

The static bridge weigh-in-motion (BWIM) systems are mostly based on strain measurements and are particularly suited for stiff short-span bridges. Recently, the BWIM systems based on acceleration measurements are developed for long-span bridges because of the portability and low-cost of accelerometers as compared to strain gauges. Although, these BWIM systems can estimate the gross vehicle weights (GVWs) with high accuracy, but they fail to identify the weights of individual axles accurately especially for vehicles with closely spaced axles. In this paper, an iterative linear optimization problem (ILOP) was proposed to accurately identify the individual axle weights and GVWs of vehicles traversing a bridge. A BWIM system consisting of only microelectromechanical system (MEMS) accelerometers was employed in an in-service steel girder bridge having multiple lanes. The proposed method used the bridge displacement responses as the measured responses which were determined from the recorded acceleration data. The information about the vehicle speed, number of axles and axle spacings were obtained by identifying the peaks in the recorded acceleration data. The effectiveness and accuracy of the proposed method were demonstrated through field tests using the four-axle test vehicles with closely spaced axles. The results showed that the axle weights of vehicles with closely spaced axles could be identified with much better accuracy by the proposed method as compared to classic BWIM systems which are based on Moses’ original algorithm.

Acetylation of Cyclodextrin-Threaded Polyrotaxanes Yields Temperature-Responsive Phase Transition and Coacervate Formation Properties.

Stimuli-responsive materials have received considerable attention for their application in biomedical fields. Herein, the temperature-dependent phase transition behavior of acetylated polyrotaxanes (Ac-PRXs) consisting of acetylated cyclodextrins threaded onto a linear axle polymer in aqueous solution is investigated. The aqueous solutions of Ac-PRXs exhibit temperature-dependent transmittance changes when the degree of acetylation is 30-40%. Upon increasing the temperature, Ac-PRXs are dehydrated and acetyl groups are subsequently associated through hydrophobic interactions. Interestingly, the Ac-PRXs form coacervate droplets with a diameter of ≈2-3 µm above their cloud points. These temperature-responsivities of Ac-PRXs are observed for other PRXs with different axle polymer molecular weights and compositions. Consequently, the acetylation of PRXs is an attractive chemical modification for yielding temperature-responsivities, and Ac-PRXs can be applied as temperature-responsive supramolecular materials.