Axle Spacing Research Articles

Determination of stripping zones and axle spacing between the cylinders in two-cylinder stripping reaper (header)

Stripping devices become an alternative to harvester-threshers in the harvesting season. The research objective is improvement of harvesting operations, including the introduction of the method of soya stripping. The research showed that the use of a single-cylinder stripping device was not optimal, for harvesting losses exceeded agricultural norms. It was decided to use a two-cylinder scheme. The implementation of this design solution reduced the grain loss rate, but still it was not enough to achieve compliance with the requirements of classical soybean harvesting. For high-quality soybean stripping, it is necessary to calculate the zones of stripping and axle spacing between the first and second stripping cylinders, as well as the profile of the stripping combs. Schemes presented are calculation of the stripping zones and determination of the axle spacing between the cylinders, taking into account their positional relationship in the stripping reaper.

Determination of the Helical-Gear Geometry Taking Account of Total Teeth Wear

Formulas are proposed for the worn-layer thickness sum in contacting rapidly wearing sections of the active teeth surfaces in driving and driven helical gears, on the basis of involute-gear theory, taking account of the laws governing wear. A relation is found between the rack displacement coefficients of helical gears such that the largest sum of wear rates in those sections is minimized. Numerical solution of the equations for a helical gear with standard axle spacing permits determination of the values of the displacement coefficients such that the duration of gear operation up to the permissible worn-layer thickness sum in those sections is maximized.

Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements

Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the ‘nothing on the road’ principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.

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.

Bridge health monitoring using deflection measurements under random traffic

This paper demonstrates the potential to use measured deflection responses of a bridge to random traffic as a means of monitoring its structural health. Three displacement transducers are simulated on the bridge at different locations, providing absolute deflection data. The stiffness profile through the length of the bridge is calculated using these data. Vehicle axle loads are calculated as well as the relative bridge stiffness profile using a combined iteration approach. Axle spacings for each vehicle are currently measured in Bridge Weigh-in-Motion systems and are presumed to be known here. After estimating axle weights for each passing vehicle, the profile of bridge stiffness is calculated from batches of 1,000 vehicles with a focus on detecting any local damage. A moving average filter and a filtering method based on the Blackman window are used to reduce dynamic effects and the influence of noise. This stiffness profile approach is shown to be suitable for both damage location and quantification.

Assessment of railway plain concrete arch bridges subjected to high-speed trains

There are plentiful masonry arch bridges around the world that have been used as railway bridges for more than 80 years. In Iran's railway network, 3,700 bridges exist which all of these have been designed based on service load in the past (low-speed trains) and are still serving for trains at a maximum speed of 100 km/h. In the present study, two plain concrete arch bridges are investigated using finite element technique. For validation of numerical models, the crown deflection of the two bridges under static loading and the locomotive moving as well as the first three modes of the bridges were selected as a calibration criterion and compared with experimental results. In the second step, by categorizing the trains based on geometrical properties, 27 models of Asian and European high-speed trains and Euro-code suggestions have been detected to make sure all of the possible geometries have been counted. Afterward, 324 time-history dynamic analyses have been performed in order to assess the dynamic behavior of the bridges under the high-speed trains at speeds of 150, 200, 250, 300, 350 and 400 km/h. Finally, deflection and acceleration responses of the bridges for all 324 dynamic analyses at the main crown of the bridges have been extracted. The results indicate that the dynamic performance of these bridges under high-speed trains is affected by span length, vehicle motion speed, material stiffness, train coach distribution, and axles’ distances. The responses are increased by increasing the vehicle speed and the spacing of axles in bogies. In the case of acceleration and displacement responses, the effect of span length parameter appeared to be significant at every speed. The results achieved from the time-history analyses showed more sensitivity in the dynamic performance of the bridge with longer span length (2L20) to acceleration response and in the bridge with lower span length (5L06) to displacement response.

The role of bending stress on the initiation of reverse transverse defects

This paper investigated the role of longitudinal reverse bending stress on the initiation of reverse transverse defects. The longitudinal reverse bending stress occurs due to the reverse bending of the rail between two-wheel passage leading to the generation of tensile bending stress at the railhead and the lower head areas. The longitudinal bending stress was investigated as part of a parametric study on the rail cant angle, rail stiffness, lateral-to-vertical load ratio, and rail profile. A finite element model was created by using ABAQUS to analyze the extent of reverse bending in rails with respect to the chosen set of parameters. Under different lateral-to-vertical load ratios of 0, 0.3, 0.5, and 0.7, the maximum stress at the rail lower gauge corner was found to vary between 14.57MPa and 15.47MPa under the reverse bending condition. Similarly, low values of tensile stress under the reverse bending scenario were observed with changes in the rail cant angle and axle spacing with respect to different coal and iron ore wagons. The results revealed that the magnitude of the bending stress under different conditions of reverse bending was not significant enough to initiate a crack at the lower gauge corner.

Temporal Variation of Ground-Borne Vibrations in Ballasted High-Speed Railway Embankments

The ascending requirement of railway passengers for fast transit to distant stations has led to the development of high-speed railway networks. Faster and longer trains with heavy axle loads are in operation, inducing stronger ground vibrations in the supporting railway infrastructure. The conventional design of railway track structures are based on the maximum displacements safely allowed in the track under the expected railway loads. However, the vibrations generated in the tracks due to railway operations should also be of concern. This study characterizes the peak particle velocities (PPVs) generated in the component strata of a ballasted railway embankment during high-speed train transit within 160 to 200 km/h. The transient response of each strata is characterized for transit of wheel load in the vibration influence zone, and temporal variation of PPVs have been logged for the transit of wheel load through a distance of 30 m on the railway track. The results from the present study indicate that railway track embankments are subject to strong ground motions during high-speed train transit, which will lead to accelerated degradation of the track structure. Ground vibrations are remnant in the railway embankment, even after passage of train wheels to farther distances. Deployment of vibration mitigation measures are recommended in sensitive locations proximate to railway lines. The particle velocities quantified in this study can be utilized by railway design authorities for estimating ground-borne railway vibrations under multiple train wheel configurations, based on the axle spacing, bogie spacing and the depth of the track under consideration.

Development of an overweight vehicle permit fee structure for Illinois

Permits are one of the most effective and common tools for state agencies to regulate the operation of overweight and oversize trucks to ensure the safety of passenger and freight traffic and to minimize damage to pavements and bridges. Although the State of Illinois uses a relatively comprehensive permit system, many of its parts have not been revised for more than 30 years. Therefore, the objective of this study was to evaluate up-to-date impacts of overweight (OW) trucks and develop an equitable permit system. In this study, impacts of OW trucks on bridges, pavements, and traffic safety were evaluated; and individual fees were developed for each. The most recent databases on infrastructure condition and state-of-the-art prediction algorithms were employed to produce realistic and up-to-date assessment of OW trucks’ impact. Finally, two approaches were recommended to combine individual fees. The first approach requires developed frameworks for calculating individual fees to be integrated with existing geographic information system-based online permit tools that are currently used by many departments of transportations (DOTs). These tools compute the fee for each vehicle by reporting a route based on user-input origin/destination. The information specific to each OW vehicle (e.g., mile, axle spacing, weight, bridge inventory rating) can be extracted from the reported routes and injected into the developed frameworks. Thereby, the fee can be computed in an equitable and more accurate way. The second approach is converting the frameworks into a formula to conform to the conventional permit fee calculation schemes (e.g., table format).

Some observations on BWIM data collected in Manitoba

Three highway bridges in the Canadian province of Manitoba are being monitored continuously not only for their long-term performance but also for bridge weighing-in-motion (BWIM). Data collected for the BWIM study has led to some observations that have far-reaching consequences about the design and evaluation loads for highway bridges. This paper presents the well-known concept of equivalent base length, Bm, as a useful tool for comparing trucks with different axle weight and spacing configurations as they influence load effects in all bridges. It is discussed that the statistics of gross vehicle weights (GVWs), W, collected over a one-month period is not significantly different from that for the GVW data collected over a longer period. A rational method concludes that the value of W for the CL-W Truck, the design live load specified by the Canadian Highway Bridge Design Code, is 555 kN for Manitoba. The observed truck data in Manitoba presented on the W–Bm space is found to be similar to that collected in the Canadian province of Ontario more than four decades ago. It was also found that the multi-presence factors, accounting for the presence of side-by-side trucks in two-lane bridges, specified in North American bridge design and evaluation codes are somewhat conservative.

Collecting comprehensive traffic information using pavement vibration monitoring data

AbstractTraffic data is essential for intelligent traffic management and road maintenance. However, the enormous effort used for data collection and analysis, combined with conventional approaches for traffic monitoring, is inefficient due to its high energy consumption, high cost, and the nonlinear relationships among various factors. This article proposes a new approach to obtain traffic information by processing raw data on pavement vibration. A large amount of raw data was collected in real time by deploying a vibration‐based in‐field pavement monitoring system. The data was processed with an efficient algorithm to achieve the monitoring of the vehicle speed, axle spacing, driving direction, location of the vehicle, and traffic volume. The vehicle speed and axle spacing were back‐calculated from the collected data and verified with actual measurements. The verification indicated that a reasonable precision could be achieved using the developed methods. Vehicle types and vehicles with an abnormal weight were identified by a three‐layer artificial neural network and the k‐means++ cluster analysis, respectively, which may help law enforcement in determining on an overweight penalty. A cost and energy consumption estimation of an acceleration sensing node is discussed. An upgraded system with low cost, low energy consumption, and self‐powered monitoring is also discussed for enabling future distributed computing and wireless application. The upgraded system might enhance integrated pavement performance and traffic monitoring.

Identification of moving train loads on railway bridgebased on strain monitoring

Moving train load parameters, including train speed, axle spacing, gross train weight and axle weights, are identified based on strain-monitoring data. In this paper, according to influence line theory, the classic moving force identification method is enhanced to handle time-varying velocity of the train. First, the moments that the axles move through a set of fixed points are identified from a series of pulses extracted from the second derivative of the structural strain response. Subsequently, the train speed and axle spacing are identified. In addition, based on the fact that the integral area of the structural strain response is a constant under a unit force at a unit speed, the gross train weight can be obtained from the integral area of the measured strain response. Meanwhile, the corrected second derivative peak values, in which the effect of time-varying velocity is eliminated, are selected to distribute the gross train weight. Hence the axle weights could be identified. Afterwards, numerical simulations are employed to verify the proposed method and investigate the effect of the sampling frequency on the identification accuracy. Eventually, the method is verified using the real-time strain data of a continuous steel truss railway bridge. Results show that train speed, axle spacing and gross train weight can be accurately identified in the time domain. However, only the approximate values of the axle weights could be obtained with the updated method. The identified results can provide reliable reference for determining fatigue deterioration and predicting the remaining service life of railway bridges.

The identification technology of vehicle weight based on bridge strain time-history curve

It is the basic characteristics of bridge weigh-in-motion technology to directly identify the vehicle weight based on the bridge dynamic response. At present, bridge weigh-in-motion technology tends to be mature in identification of gross vehicle weighing, but there is no breakthrough in identification of single-axle weighing. Therefore, a new axle-weight identification method is proposed using bridge weigh-in-motion technology in this article, in which the idea of bridge weigh-in-motion technology is introduced first. The numerical expression of the single-axle weight and the identification expression of axle space and vehicle speed are presented thereafter. Furthermore, the accuracy of the presented method is further reinforced through a series of practical model experiments of simply supported and continuous beam. The experimental result indicated that the proposed method is feasible in practical application.

Probabilistic bridge weigh-in-motion

Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, 200 randomly generated 2-axle trucks pass over a 6 m long simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, 77 pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM.

Numerical investigations into the behavior of light rail bridges

This paper presents various technical aspects related to bridges under light rail gravity loadings, including flexural and shear responses, deflection, serviceability, and dynamic load allowance. Unlike highway and conventional rail bridges, the behavior of light rail bridges has scarcely been reported; thus, limited information is available. Five types of benchmark bridges are designed with steel plate girders, prestressed concrete boxes, reinforced concrete T-girders, prestressed concrete I-girders, and closed steel box girders. Three-dimensional finite element models are developed to predict the behavior of these bridges when loaded by four representative light rail trains operated in the United States (Colorado, Massachusetts, Minnesota, and Utah), which results in 4,932 cases. Parameters for investigations involve structural configurations (simply-supported and continuous spans), geometries (girder spacing, span length, curvature, and skew), and loading characteristics (one-track-loaded and two-track-loaded with one to four articulated trains). A comparative study is conducted to evaluate the applicability of existing design specifications (highway and heavy-haul train loadings) that are frequently referenced in light rail bridge design. The flexural moment of the bridges is controlled by span length, the number of loaded tracks, and the axle spacing of the articulated trains. Contrary to the implications of horizontal curvature, those of skew angles are significant in altering the responses of the bridges concerning moments and fundamental frequencies. The deflection criteria of the existing specifications are not applicable to the light rail bridges; consequently, an alternative approach is suggested. Regarding dynamic load allowance, the predicted values are generally lower than those used in practice.

Nothing-on-Road Axle Detection Strategies in Bridge-Weigh-in-Motion for a Cable-Stayed Bridge: Case Study

This case-study article aims to share the field-test observations of a real-world cable-stayed bridge with the research community of bridge-weigh-in-motion to address the challenges of axle identification. Various structural members of the bridge, including cables, girders, and the deck, were instrumented with strain gauges at different locations to measure the axial, bending, or shear strain responses. Numerous field tests were conducted by running light and heavy vehicles traveling at different speeds, in different traffic directions, and in different lateral locations on the bridge. Because the identification of closely spaced axles is important to ensuring true classification of the vehicles, vehicles with tandem- and tridem-axle configurations were adopted in the field test. The study aimed to identify the sensor arrangement through which the closely spaced axles can be reliably detected regardless of the speed, traveling direction, and lateral location of the vehicle on the bridge. It was found that the axial strains on the cables and bending strains in the girders provided the global response of the structure; hence, they were unable to identify the closely spaced axles. In contrast, it was observed that the longitudinal strains under the deck were able to identify the closely spaced axles, provided they were positioned as closely as possible to the wheel path. Finally, the shear responses at the end of the span were able to identify the closely spaced axles irrespective of the traveling direction and lateral location of the vehicle. In this study, due to the testing limitations, including the short span of the bridge and the presence of a roundabout at one end of the bridge, it was not feasible to maintain a constant speed; therefore, identification of axle weight and axle spacing, which requires a constant-speed assumption, is not discussed.

Reliability-based design optimization of a vehicular live load model

Typically, RBDO is used to select physical characteristics of a structure in order to optimize performance under uncertainty, where design variables are often taken as geometric or material parameters defining the structure. In this study, the effectiveness of using RBDO to develop a design load model is explored, which is to be used to design a wide range of bridge girders subjected to location-specific traffic loads. The objective is to minimize variation in reliability index among different bridge girders designed using the load model. Design variables are taken as the number of axles, axle spacing, and axle weights of the design vehicle representing the live load model. A probabilistic constraint is imposed, limiting the minimum reliability index of each girder design. Random variables considered are girder resistance in moment and shear, and bridge dead load and vehicular live load components. A single loop procedure is implemented by using a non-iterative, modified reliability method, the final results of which are verified with direct Monte Carlo Simulation. The optimization problem is solved with a genetic algorithm. It was found that procedure employed could be used to develop a design load model that resulted in substantial improvement over the design models in current use, where the optimized models significantly reduced the range and coefficient of variation of reliability index among the bridge cases considered.

DYNAMIC IMPACT OF HEAVY LONG VEHICLES WITH EQUALLY SPACED AXLES ON SHORT-SPAN HIGHWAY BRIDGES

Extremely large trucks with a weight exceeding the standard require a permit before they are allowed to cross the bridges of a specific route. For the purpose of safety, an escort is often employed to maintain a distance between vehicles and to ensure that the bridge load remain below the allowed maximum. Given that the speed of these large vehicles is quite slow and that the amplitude of vibrations normally declines when the vehicle mass is large, a minor dynamic amplification of the bridge response is expected. However, some of these large trucks have a unique feature characterized by “multiple equally-spaced axles”, something that is uncommon in normal vehicle. The application of axle forces at equal intervals can dynamically excite bridges to a considerable extent, even at low speeds. These “critical” low speeds are estimated a priori from the axle spacing of the truck and the main frequency of vibration of the bridge. This paper demonstrates that when the “critical” speed is unavoidable, a relatively high dynamic allowance must be added to static calculations before granting a permit to a long heavy vehicle.

Comparison of vehicle re-identification models for trucks based on axle spacing measurements

ABSTRACTIn previous research, it has been demonstrated that there is enough variation within the truck population in terms of axle spacings and vehicle lengths, which enable anonymous vehicle re-identification between two measurement stations (e.g., two weigh-in-motion (WIM) sites). Matching trucks between two sites can support various applications, such as calibration of WIM equipment and estimation of travel times and origin-destination flows. In this paper, several modeling approaches to solve the re-identification problem are explored including Naïve Bayes (NB), Bayesian Models (BM) fitted by mixture models, and the formulation of the re-identification problem as a mathematical assignment problem. In addition, the influence of selecting a similarity measure is evaluated through numerical experiments conducted on real-world data from six pairs of upstream–downstream WIM stations. The results demonstrate that solving the re-identification problem with BMs fit by mixture distributions outperforms solving with NB models, while both are outperformed by the mathematical assignment formulation of the same problem, especially when vehicle-pairs exceeding a high threshold of similarity are matched. In addition, expressing the similarity between measurements from two stations as a percentage difference is found to be relatively more advantageous. For the presented pairs of WIM stations, up to 90% matching accuracy can be achieved when the best combination of re-identification method and similarity measure are implemented, and only those vehicle-pairs exceeding a high threshold of similarity are matched.AbbreviationsAAAssignment AlgorithmBMBayesian ModelDDownstreamGMMGaussian Mixture ModelNBNaïve Bayes MethodpdfProbability Density FunctionUUpstreamVRIVehicle Re-Identification

A MECHANISTIC-EMPIRICAL IMPACT ANALYSIS OF DIFFERENT TRUCK CONFIGURATIONS ON A JOINTED PLAIN CONCRETE PAVEMENT (JPCP)

Until the last decade, the 1993 American Association of State Highway and Transportation Officials (AASHTO) design guide has been traditionally used for the design of flexible and rigid pavements in the USA and some parts of the world. However, because of its inability to meet the new traffic and material challenges, a Mechanistic Empirical Pavement Design Guide (MEPDG) was introduced based on an NCHRP 1-37 A study conducted in 2004. This study used the MEPDG software and associated models to determine, through comparative truck damage analysis, the effects of nine different truck configurations on a 12 inch-jointed plain concrete pavement (JPCP). The study recorded truck damages at the end of each analysis period (40 years) and comparatively analyzed the relative pavement damage in terms of fatigue cracking, faulting, and surface roughness. The results indicated that the most critical damage to the concrete pavement was caused by truck cases with high and uneven load distribution and relatively smaller size axles group (e.g. tandem). Other key findings included the following; (1) increase in damage when the truckloads were shifted between the same size axles, (2) decrease in truck damage when the truckloads were shifted from tandem axle to quad axles, and (3) no change in truck damage when the axle spacing was increased between wheels of a quad axle.