Bridge Weigh-in-motion Research Articles

Accurate measurements of gross vehicle weight through bridge weigh-in-motion: a case study

In a bridge design code, the design live loads should be based on actual traffic loads. In the past, the information about the actual truck loads on highway bridges was obtained from truck surveys, in which the trucks are stopped for measurement and weighed on ‘static’ weighing scales. During the past few decades, however, the information about truck loads is collected either by pavement weigh-in-motion or bridge weigh-in-motion (BWIM) systems. This paper provides details of three BWIM methods, which were calibrated with extensive testing on a slab-on-girder bridge in Winnipeg, Canada. The first two methods are based on the assumption that truck loads could be represented by an equivalent uniformly distributed load. The first method uses the asymmetry in the shape of the bending moment diagram to calculate the gross vehicle weight (GVW), while the second method compares the girder response at two instrumented transverse sections. The third method calculates GVW using strain signal area as proposed by Ojio and Yamada in 2002. The results show that the first method was inconsistent in its accuracy; the second method’s accuracy reduced with the length of the vehicle. The third method proved consistent and accurate to within 5 % for the test trucks provided that the vehicle speeds could be estimated accurately. Accurate calculations of the truck speeds were achieved by using the time delay in the peak responses of the straps that confine the externally restrained deck slab of the bridge.

Bridge Influence Line Estimation for Bridge Weigh-in-Motion System

This paper presents an algorithm that estimates the influence line (IL) of a bridge using data collected when trucks pass over the sensors installed in the bridge. The algorithm is tested with data collected from the Millau Viaduct in France using a bridge weigh-in-motion (B-WIM) device. The algorithm uses the maximum likelihood estimation (MLE) and is compared with an old algorithm. The algorithm is more robust because it takes into account many signals for the estimation of the IL.

On the use of bridge weigh-in-motion for overweight truck enforcement

Bridge weigh-in-motion (B-WIM) is a method by which the axle weights of a vehicle travelling at full highway speed can be determined using a bridge instrumented with sensors. This paper looks at the history of B-WIM, beginning with early work on weigh-in-motion (WIM) technologies in the 1960s leading to its invention by Fred Moses and George Goble in the USA in the mid 1970s. Research initiatives in Australia and Europe have focused on improving B-WIM accuracy. The moving force identification (MFI) method models the dynamic fluctuation of axle forces on the bridge and holds particular promise. B-WIM accuracy depends on bridge site conditions as well as the particular data processing algorithm. The accuracy classifications of several B-WIM installations reported in the literature are summarised in this paper. Current accuracy levels are sufficient for selecting vehicles to be weighed using static scales, but insufficient for direct enforcement.

Monitoring of Changes in Bridge Response Using Weigh-In-Motion Systems

Weigh-In-Motion (WIM) and Bridge Weigh-In-Motion (B-WIM) are systems that allow obtaining the axle weights of road vehicles in motion, at normal traffic speeds. While WIM employs sensors embedded in the road pavement, B-WIM use the strain recordings of a bridge to infer the traversing vehicle axle weights. Both systems have been heavily improved over the past decades, and commercial versions are currently in operation. The two main applications of these systems are: (1) to assess the traffic loading on the infrastructure, and (2) to enforce the maximum weight limits. This paper suggests a novel application of these two systems to identify changes in bridge stiffness. It requires the bridge to be instrumented with a B-WIM system and a WIM system nearby. The principle is to use both systems to evaluate the gross weight of vehicles passing over the bridge and correlate their predictions. Changes in correlation of the predicted axle weights over time will indicate either structural damage or faulty sensor. A finite element model of a coupled vehicle-bridge system with different damage scenarios is used to test the approach numerically. Vehicle mechanical properties and speeds are randomly sampled within a Monte Carlo simulation. Results show how correlation changes as damage increases and how this correlation can be employed as a damage indicator.

Determination of Dynamic Amplification Factors Using Site-Specific B-WIM Data

The dynamic amplification factor (DAF) is a significant parameter for new bridge design and characterization of existing bridges. The AASHTO code provides a predicted value based on an assumed structural model, but not one relative to actual bridge behaviors and field-testing results. Recently, the Safe and Efficient Transportation Act (2011) increased the traffic truck weight from 355.84 to 431.5 kN (80 to 97 kips). Thus, improved methods are needed to determine accurate DAF values for new bridge designs and to evaluate the site-specific DAF values of existing bridges. This paper presents a simulation method to evaluate the DAF values of existing bridges using bridge weigh-in-motion (B-WIM) data. This approach includes the static moment obtained from the field-calibrated simulation model and the dynamic moment obtained from the B-WIM experimental data. A model for multiple vehicles is developed to simulate the traffic intensity, and to predict the DAF value of an existing bridge. Presented are the experimental findings and determination of the site-specific DAF value.

Theory and case study of vehicle load identification based on BWIM of steel truss bridge

AbstractIn China recent accidents involving sudden collapses of bridges have aroused great concern about real traffic loads and the loadbearing capacities of existing bridges. However, the proper management of such bridges relies on accurate information on bridge operating loads, which is essential for assessing correctly the operation and safety of bridges. Therefore, this paper proposes an integrated load identification system for operating traffic which is based on the characteristics of a steel truss bridge, i.e. the finite distribution of stress influence lines of suspender and orthotropic steel deck, and employs the BWIM (bridge weigh‐in‐motion) technique to establish the functional relationship between vehicle load and stress history as well as the functional relationship between axle load and stress history.To verify the system, a stress monitoring case study was carried out on two steel truss bridges under both controlled and normal traffic conditions. In the case of controlled traffic, monitored information is used to back‐calculate vehicle load and axle load. The load information is then compared with a synchronous video recording of the traffic to check back‐calculated data about vehicle weight, vehicle speed, wheel weight and the deviation between lateral distribution and actual condition, coupling the relationship between vehicle load and corresponding axle load in order to assure the validity of the identification system established. In the case of normal traffic, monitored stress data are used to back‐calculate vehicle load, axle load, vehicle speed and wheel lateral distribution, which are further analysed to work out a probability distribution so that it can directly help assess the real load capacity and fatigue life of the bridges.

Testing of a Bridge Weigh-in-Motion Algorithm Utilising Multiple Longitudinal Sensor Locations

Abstract A new bridge weigh-in-motion (WIM) algorithm is developed which makes use of strain sensors at multiple longitudinal locations of a bridge to calculate axle weights. The optimisation procedure at the core of the proposed algorithm seeks to minimise the difference between static theory and measurement, a procedure common in the majority of bridge WIM algorithms. In contrast to the single unique value calculated for each axle weight in common Bridge WIM algorithms, the new algorithm provides a time history for each axle based on a set of equations formulated for each sensor at each scan. Studying the determinant of this system of equations, those portions of the time history of calculated axle weights for which the equations are poorly conditioned are removed from the final reckoning of results. The accuracy of the algorithm is related to the ability to remove dynamics and the use of a precise influence line. These issues are addressed through the use of a robust moving average filter and a calibration procedure based on using trucks from ambient traffic. The influence of additional longitudinal sensor locations on the determinant of the system of equations is discussed. Sensitivity analyses are carried out to analyse the effect of a misread axle spacing or velocity on the predictions, and as a result, the algorithm reveals an ability to identify potentially erroneous predictions. The improvement in accuracy of the calculated axle weights with respect to common approaches is shown, first using numerical simulations based on a vehicle-bridge interaction finite-element model, and second using experimental data from a beam-and-slab bridge in Slovenia.

Feasibility of a Multi-Functional Bridge Bearing with Built-in Piezoelectric Material

This paper presents a multi-functional bridge bearing conceived by introducing a piezoelectric (PZT) element within conventional pot bearing or rubber elastomeric bearing. Bridge bearings are devices that transfer loads and movements from the deck to the substructure and foundations of a bridge. Bearings are thus continuously subjected to the loads of vehicles crossing the superstructure, and they are also subjected to the weight of the superstructure. The proposed bearing has been developed to respond to the increasing demand for self-powered sensing devices for the monitoring of bridges by using the vibration-based mechanical energy produced by the traffic load. This PZT element not only supplies the electrical power required for the operation of the embedded sensing and lighting systems but can also be used to perform real-time monitoring of the traffic by adopting built-in load measuring functions such as those in bridge-weigh-in-motion (BWIM) systems. The feasibility of the proposed system is discussed through its application to an example bridge.

Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem

Moving Force Identification (MFI) theory can be used to create an algorithm for a Bridge Weigh-in-Motion (WIM) system that can produce complete force histories of the loads that have traversed a bridge structure. MFI is based on general inverse theory, however, and calibration of such a system requires a complete Finite Element (FE) model of the bridge to be available for implementation in the field. This is something that is often infeasible in practice as FE models created using theoretical values for material properties bear a poor relation to reality. The Cross Entropy optimisation method has been adapted here to address this calibration problem. The general system FE global mass and stiffness matrices of the bridge FE model are found by best fit optimisation to match field measurements. In this fashion a fully automated calibration procedure is developed for an MFI algorithm. This system is tested theoretically using three different FE plate models, coupled with a 3-dimensional vehicle model, allowing for Vehicle–Bridge Interaction (VBI).

Modelling of Rutting Development in Pavement Structures

A mechanistic empirical (M–E) approach has been developed and thereafter used to calculate the rutting performance of an arterial road in Southern Sweden. The results were then compared with measurements from the Swedish long term pavement performance (LTPP) database. The arterial road had reached the critical 15mm rut after 18 years in operation. The M–E approach used is a two–step procedure where the response of the structure is calculated mechanistically and thereafter the performance predicted empirically based on scaling of laboratory test results. Extensive laboratory testing was carried out on samples taken from the test road. Traffic counting and Bridge Weigh–in–Motion (BWIM) data were used to determine the amount of traffic loading, and data from weather stations were used to take into account the temperature dependency of the asphalt bound layers. The analysis shows that the rutting development can be simulated adequately although the calculations show slower rate than the measurements towards the end of the simulated period. The discrepancy in the rate of rutting between the measurements and the observations that was observed after about 9 years of operation might be due to the fact that no ageing or disintegration in material characterization was incorporated in the numerical analysis but surely observed in reality.

밀도추정함수와 평균보정계수를 이용한 BWIM 알고리즘의 현장실험 적용

본 논문은 변형률 계측데이터를 사용하는 신뢰성 및 정확성을 증진된 BWIM(Bridge Weigh-In-Motion) 알고리즘을 개발하고, 이를 교량에 대한 다양한 실험을 통해 검증하고자 하는 것이다. 본 논문에서는 밀도추정함수와 평균보정계수를 이용한 BWIM 알고리즘을 제시한다. 밀도추정함수는 다축하중을 추정할 때 신뢰할 수 있게 적용할 수 있음을 입증하였으며, 평균보정계수는 이론 계산된 모멘트와 계측된 변형률에서 계산한 모멘트 사이의 전반적인 오차를 최소화하기 위해 적용된다. 개발된 알고리즘은 수치예제, 실내모형실험 그리고 다주형 합성교량에 대한 현장실험을 통해 성공적으로 검증하였다. The paper aims at developing a more reliable and accurate BWIM(Bridge Weigh-In-Motion) algorithm using measured strain data and examining its efficiency with various tests on bridges. It proposes a BWIM algorithm using density estimation function and average modification factor for moment-strain relationship. Density estimation function has been proved to be reliably applied when multiple axle loads are estimated. An average modification factor is applied to minimize overall error that can be encountered between theoretically computed moments and measured strains at multiple locations in a bridge. The developed algorithm has been successfully examined through numerical simulations, laboratory tests, and also by field tests on a multi-girder composite bridge.

Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

BWIM 시스템을 사용한 사장교의 차량하중 분석

본 논문에서는 교량 모니터링 시스템의 일부분으로 서해대교에 설치된 교량 하중측정 시스템(BWIM system)으로부터 획득한 신호를 분석하여 통행차량의 정보를 추출하기 위한 알고리즘의 개발과정과 이를 위해 수행한 현장 차량주행시험에 대하여 기술하였다. 개발된 BWIM 시스템은 포장층에 매설하는 축감지기가 없는 형태로, 바닥판과 가로보에 설치된 변형률계로부터 측정한 시간이력 변형률신호만을 이용하였다. 이들 측정신호로부터 추출하고자 하는 차량의 정보는 통과차로, 통과속도, 차 축수 및 총 중량이며, 이들 정보의 추출을 위해 패턴인식기법의 일종인 인공신경망(Aritificial Neural Network, ANN) 기법을 사용하였다. 현장 차량주행시험을 통하여 기지차량 및 미지차량 통행시의 BWIM 응답 데이터를 측정하였으며, 이들 실측데이터를 사용하여 인공신경망의 학습 및 성능검증을 수행하였다. 개발된 기법을 사용하여 추출되는 차량의 정보들은 현재의 교량상태 및 피로수명 평가시 활용될 수 있을 것이며, 향후 설계트럭 하중모델의 개정시 기초자료로도 활용될 수 있을 것으로 기대된다. This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.

Wavelet domain analysis for identification of vehicle axles from bridge measurements

Bridge weigh-in-motion (B-WIM) is a process by which the axle and gross vehicle weights of vehicles travelling at highway speeds can be determined from instrumented bridges. The traditional method of attaching strain transducers to the soffit of the bridge and placing axle detectors on the road surface has been replaced here by using additional transducers underneath the bridge for axle detection and nothing-on-the-road (NOR). This paper presents a wavelet based analysis of strain signals and shows the efficacy of using wavelets in pattern recognition of these signals. The transformed signals are used to identify axle passage and hence the vehicle velocity and the axle spacing. In addition to numerically generated strains, signals acquired from such a NOR instrumentation of a bridge in Slovenia have been analysed by the method of wavelet transformation to extract axle position information that was not readily detectable using existing methods.

2径間連続鋼桁橋を用いたBWIMの精度

For constructing a good maintenance scheme of an existing bridge, the methods for estimating the weights of heavy trucks are needed and Bridge Weigh-In-Motion (BWIM) has attracted many researchers. For the accuracy of this method, the usage of a short simply-supported straight steel bridge is preferred. However, the highway of our interest does not havc stccl bridges except the one that is a continuous skew plate-girder bridge. Because of this, we first investigate the accuracy of BWIM carefully when applied to this bridge: we use three trucks of known weights and carried out 5 different patterns of running test. Each pattern comprises multiple cases with different running conditions. Using strains obtained in this running test, we conduct BWIM and estimate thick weights. It is found that the error of a truck weight is less than 5% when a single truck is running while it can be up to 11.7% when two trucks are running. The influence of the number of data pints on the accuracy of WIM is also studied. From a practical point of view, this much of error is acceptable. The usage of a continuous skew steel-plate-girder bridge for Bridge Weigh-In-Motion can be justified

Testing of Bridge Weigh-In-Motion System in a Sub-Arctic Climate

Abstract Systems for weighing vehicles while they are in motion are in widespread use in many countries. The accuracy of these weigh-in-motion (WIM) systems is strongly influenced by the road profile and vehicle dynamics. Systems based on sensors that are embedded in the pavement or placed on top of the road surface can measure the axle load only for the fraction of a second for which the wheels are present on the sensor. An alternative to pavement WIM systems that increases the length of the load-sensitive element is to use an existing bridge as a weighing scale (Bridge WIM). A major test of a Bridge WIM system at a test site near the Arctic Circle is described in this paper. The test was conducted along-side a larger test of pavement WIM systems. A large number of trucks from random traffic were weighed statically and the results compared to those from the Bridge WIM system. The accuracy of the system is assessed in accordance with the COST 323 WIM specification, which provides a standardized method of accuracy classification. The Bridge WIM system is proven to perform satisfactorily and consistently for a wide range of temperatures in near-Arctic climatic conditions.

Model Validation for Bridge-Road-Vehicle Dynamic Interaction System

The dynamic response of highway bridges subjected to moving truckloads has been observed to be dependent on (1) dynamic characteristics of the bridge; (2) truck configuration, speed, and lane position on the bridge; and (3) road surface roughness profile of the bridge and its approach. Historically, truckloads were measured to determine the load spectra for girder bridges. However, truckload measurements are either made for a short period of time [for example, weigh-in-motion (WIM) data] or are statistically biased (for example, weigh stations) and cost prohibitive. The objective of this paper is to present results of a three-dimensional computer-based model for the simulation of multiple trucks on girder bridges. The model is based on the grillage approach and is applied to four steel girder bridges tested under normal truck traffic. Actual truckload data collected using a discrete bridge WIM system are used in the model. The data include axle loads, truck gross weight, axle configuration, and statistical data on multiple presence (side by side or following). The results are presented as a function of the static and dynamic stresses in each girder and compared with code provisions for dynamic load factor. The study provides an alternate method for the development of live-load models for bridge design and evaluation.

SITE-SPECIFIC TRUCK LOADS ON BRIDGES AND ROADS.

Condition assessment of bridge structures, prediction of remaining life and evaluation of pavement performance and behaviour require knowledge of load and resistance statistical parameters. The present study addresses the need for the determination of live load on bridges and roads, and in particular the site-specific variation of the live load statistics. Truck loads were measured under normal traffic conditions at selected bridges located on interstate highways, state highways, US highways and surface streets using a bridge weigh-inmotion (WIM) system. Truck data is also available from highway weigh station logs and citation files. Stationary weigh station data is biased and may not accurately reflect the distribution of truck axle weights, axle spacing, and gross vehicle weight. Citation data is helpful in understanding the nature and extent of overloaded trucks; however, it will not be representative of the normal traffic distribution. WIM measurements of trucks can be taken discretely during normal traffic conditions, resulting in unbiased data for a statistically accurate sample of truck traffic travelling a particular highway. Results of the present study show that truck loads are strongly site specific. In addition, there is a negative correlation between law enforcement effort and the occurrence of overloaded trucks. Overloaded trucks are observed on roads not controlled by truck weigh stations. A comparison of the weigh station data, truck citation data and WIM measurements obtained in this study confirms this observation.