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Bridge weigh-in-motion systems use instrumented bridges or culverts to weigh vehicles as they pass over the structures. They also provide data to allow the calculation of several bridge performance indicators. The article starts with the basics of a bridge weigh-in-motion system and briefly describes two key bridge performance indicators, girder distribution factor and dynamic amplification factor, which are also derived from B-WIM measurements. The central part of the article focuses on monitoring of influence lines, the third key parameter that characterises the bridge performance under traffic loads. First, the method of calculating the bending moment influence lines from random heavy traffic is described. A coefficient of rotational stiffness is introduced, which defines the shape of influence lines around the supports as a linear combination of the ideal simply supported and fixed supported influence lines, to allow quantifying the influence line changes. Then the long-term monitoring of influence lines is investigated on four different single-span test bridges. The initial focus is given on the examination of the effect of temperature on the shape of influence lines. Finally, two sets of influence lines are compared on one test bridge, one from before and the other from after replacing the expansion joints and bearings. The work done so far confirms that calculating of influence lines from random vehicles with a B-WIM system is entirely feasible and that differences in their shape can be detected on single-span bridges. What remains to be investigated is the comparison of these differences to the actual damages and under which circumstances the proposed procedure can compete with or better the routine bridge inspection and the conventional monitoring techniques.

The unit influence line of a structure reflects its behaviour and changes in response to damage that may occur. An iterative algorithm is presented in this paper to obtain the shape of the instantaneous influence line of a bridge together with the relative axle loads of trucks passing overhead. One great advantage of this approach is that the need for sensor calibration with pre-weighed trucks can be avoided. The only initial information needed are the measurement data and a preliminary estimate of influence line based on engineering judgement. The concept of a so-called population unit influence line is also presented. This is an influence line that is found from a population of trucks instead of a single vehicle. An illustrative example is presented, where strain data have been collected on a reinforced concrete culvert. As well as the robustness of the proposed algorithms, the influence of temperature on the results is demonstrated. The sensitivity of the population influence line to temperature shows that it is likely to be equally sensitive to loss of stiffness in the structure.

Chapter 4 provides a good explanation of the current state-of-the-art for the influence of dynamics on bridge traffic loading. It starts with an explanation of the concepts and a review of the various definitions used in the field, such as Dynamic Amplification factor and Impact Factor. It looks at how some of the main codes of practice in the world treat dynamics. A considerable portion of the chapter deals with the statistics of dynamic amplification. It is noted that the biggest dynamic amplifications tend to occur for light vehicle loading events. A statistical approach addresses this issue and provides a more appropriate allowance for dynamics which is called Assessment Dynamic Ratio. The influence of road surface roughness is considered and the implications of a local irregularity or pothole, as sometimes happens near the end joints. A number of field measurement campaigns of dynamic amplification are reported from various countries. These largely support the findings of the numerical studies. 2021-11-09 JG: Replaced full book PDF with chapter 4 and changed title and authors

Traffic load models used for the design of new bridges are based on conservative assumptions and have not been proven efficient for assessing safety of existing bridges. In the case of existing bridges, it is reasonable to use load models that are based on bridge weigh-in-motion data which, in addition to axle loads and spacing of bridge-crossing vehicles, provide information on bridge behaviour under traffic load. This paper provides an overview of traffic load models, as well as guidelines on the use of weigh-in- motion data when assessing condition of existing road bridges.

The dynamic component of bridge traffic loading is commonly taken into account with a Dynamic Amplification Factor (DAF)—the ratio between the dynamic and static load effects on a bridge. In the design codes, this factor is generally more conservative than in reality. Recently a new method of cal- culation of this factor had been developed. Data from 15 different bridges have been analysed since then and this paper presents the results of the analyses. The background for Bridge Weigh-in-Motion is given, and the most recent method for DAF calculation is described. The sites from which the data originated are presented, and the selection of data discussed. The results of the analyses are presented and discussed and some examples of DAF calculations are shown. Data from the considered sites have invariably demonstrated a DAF decrease with increasing axle load. This is a significant result, especially for assessment of existing structures, since it is beneficial to use measured structural param- eters to optimise structural analysis.

The assessment of the existing road bridges as a part of bridge management networks has been a subject of several European research projects and actions in the last few decades. Bridge management includes a series of activities, interconnected in order to achieve optimal balancing of required costs, potential risks and overall bridge performance. This paper discusses a valuable link between a specific indicator (traffic load information calculated using Bridge Weigh-in-Motion measurement data) and the corresponding structural performance (reliability of existing bridge). It is shown that implementation of weigh-in-motion measurements in the probabilistic assessment of existing road bridges can reveal hidden bridge reserves and predict bridge reliability development over the required lifetime. Consequently, such approach can yield an unrestricted use of the bridge over a much longer remaining service life. Broader advantages lie in an improved road network management, road bridges in particular, and in a more sustainable development of infrastructure network and greater satisfaction of road users and owners.

This paper deals with accuracy and long-term stability of bridge weigh-in-motion (B-WIM) systems. Research focused on four improvements, developed within two research projects financed by the Europ...

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks), therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles&mdash, data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

Implementation of Bridge weigh-in-motion (B-WIM) measurements and probabilistic approaches in bridge assessment may reveal hidden bridge reserves and changes in reliability levels over their projected service life, providing a key input for optimized bridge management. But, as scientists and engineers and at the same time vendors of our own ideas, we need to be in position to present the results of our sophisticated methods and algorithms, expressed mostly in terms of safety or reliability indexes, probabilities of failures and similar indicators, with expected monetary savings which are decisive for an owner or an investor.In previous research, as presented at the COST TU1402 special session in Nantes 2018, in order to define the optimal management bridge strategy, a decision tree encompassing three B-WIM strategies (no B-WIM, short term B-WIM, long term B-WIM), two possible activities (no action, bridge repair/strengthening) and two possible outcomes (bridge is safe, bridge may experience failure) have been formulated.In this paper, a further development of the decision and Value of B-WIM Information analysis in the context of assessing road bridges are discussed.The focus in the present paper is directed on a detailed assessment and modelling of costs, benefits and associated indicators related to the probability of bridge failure. Costs required for the Value of Information analysis encompass: (i) the initial cost of the new bridge CBV, (ii) direct costs of bridge repair/strengtheningCrep, (iii) indirect costs of bridge non-availability due to repair measuresCN/A, and costs of B-WIM measurements (iv)CB-WIM,1 and (v)CB-WIM 2, applying short- or long-term monitoring respectively.

Modeli prometnoga opterećenja koji se primjenjuju pri proračunu novih mostova temelje se na konzervativnim pretpostavkama te se njihova upotreba u analizi postojećih mostova nije pokazala učinkovitom. Kod postojećih mostova preporučena je primjena modela prometnoga opterećenja temeljenog na mjerenju prometa u pokretu, koji osim osovinskoga opterećenja i razmaka, daje i podatke o ponašanju mosta pod prometnim opterećenjem. U ovome su radu dani pregled modela prometnoga opterećenja te smjernice o upotrebi podataka dobivenih mjerenjem prometa u pokretu prilikom ocjene stanja postojećih cestovnih mostova., Traffic load models used for the design of new bridges are based on conservative assumptions and have not been proven efficient for assessing safety of existing bridges. In the case of existing bridges, it is reasonable to use load models that are based on bridge weigh-in-motion data which, in addition to axle loads and spacing of bridge-crossing vehicles, provide information on bridge behaviour under traffic load. This paper provides an overview of traffic load models, as well as guidelines on the use of weigh-in-motion data when assessing condition of existing road bridges.

The paper provides an overview of the development of a railway bridge weigh-in-motion (B-WIM) system, one of the first of its kind for weighing trains in motion. A steel truss bridge in Poland was used for testing the system. Four trains which passed over the bridge were weighed in a rail yard in Warsaw. The conventional road B-WIM system was adapted to calculate the weights of the train carriages using the measured response from the test bridge and the accuracy of the system was assessed. Initial result showed that weights of one of the four trains of known weight were predicted very accurately, but accuracy of the other three trains was poor, with calculated carriage weights deviating by as much as 30% from their actual values. An in-depth analysis showed that these trains were changing velocity as they traversed the bridge and that the large errors were directly correlated to this changing velocity. The standard B-WIM algorithm, which assumed a constant velocity during the passage of a vehicle or train, was adjusted to allow for the effect of this changing velocity. The results improved dramatically, with the vast majority of the calculated wagon weights falling within 5% of their actual values. Further developments tailored the B-WIM algorithm for weighing trains, including the system interface that employs graphics of locomotives and wagons. The development of the railway B-WIM has been a success and has demonstrated that calculations of train weights using instrumented bridges can be efficiently performed.

The paper presents an automatic procedure for the correction of bridge weigh-in-motion (B-WIM) measurements, which are used to determine the axle loads of heavy vehicles using instrumented bridges. According to the European specifications for weigh-in-motion criteria, using this procedure the weighing results could be improved by up to one accuracy class. Whereas measurements performed on steel structures provide reliable information about the global behaviour of individual bridges, which is accounted for in the B-WIM algorithms, cracks that are present in concrete structures can, depending on their locations with respect to installed strain transducers, amplify or reduce the response. In the present work special care was taken to detect and calibrate any strain transducer which showed a disproportional response. The accuracy of the method was investigated numerically in relation to the extensive data which were available in the case of a reinforced concrete bridge (motorway underpass), located near Ljubljana, Slovenia, and then validated by a one-to-one comparison of the B-WIM weighing results and the statically weighed test vehicles.

Implementation of Value of Information (VoI) analysis regarding Structural Health Monitoring (SHM) data and corresponding assessment results of Case Study bridge is presented in this paper. SHM data (traffic and structural), obtained with bridge weigh-in-motion (WIM) measurements, are implemented in the probabilistic assessment of load carrying capacity of a Case Study Bridge. In the first part of the paper, a short overview of SHM measurements and description of Case Study Bridge is presented, along with the assessment results. Link between SHM data and the cor- responding structural performance is defined, and it is shown that application of WIM measure-ments in the assessment of existing road bridges can increase their reliability level and, therefore, extend the predicted service life of these bridges. In the second part of the paper, implementation of VoI analysis on Case Study Bridge is discussed and all the factors of interest are presented in the VoI flowchart. Furthermore, decision tree for VoI analysis is defined, along with correspondent outcomes, actions and resulting system states. Further steps and recommendations are discussed in the final part of the paper, including correla-tion factors, possible outcomes and, finally, discussion of resulting indicator as a benefit/cost ratio of Bridge WIM measurement implementation in assessment of existing bridges.

Implementation of Value of Information (VoI) analysis regarding Structural Health Monitoring (SHM) data and corresponding assessment results of Case Study bridge is presented in this paper. SHM data (traffic and structural), obtained with bridge weigh-in-motion (WIM) measurements, are im- plemented in the probabilistic assessment of load carrying capacity of a Case Study Bridge.In the first part of the paper, a short overview of SHM measurements and description of Case Study Bridge is presented, along with the assessment results. Link between SHM data and the correspond- ing structural performance is defined, and it is shown that application of WIM measurements in the assessment of existing road bridges can increase their reliability level and, therefore, extend the predicted service life of these bridges.In the second part of the paper, implementation of VoI analysis on Case Study Bridge is discussed and all the factors of interest are presented in the VoI flowchart. Furthermore, decision tree for VoI analysis is defined, along with correspondent outcomes, actions and resulting system states.Further steps and recommendations are discussed in the final part of the paper, including correla- tion factors, possible outcomes and, finally, discussion of resulting indicator as a benefit/cost ratio of Bridge WIM measurement implementation in assessment of existing bridges.

Bridge weigh-in-motion systems were introduced in the 1970s as a structural health monitoring tool for road bridges. They help bridge operators determine the cause of induced strain in bridges and collect statistics on vehicle weight, class and frequency. There have been over 3000 installations in 25 countries worldwide, which has led to vast improvements in data post-processing. However, existing systems are based on electrical resistance strain gauges, which can be prohibitive in achieving data for long-term monitoring of rural bridges due to power consumption. This paper introduces a new low-power system using fibre optic sensors, which has been pioneered in Northern Ireland. A series of fibre optic sensors were attached to the soffit of an existing integral bridge with a single span of 19 m. The site selection criteria and full installation process are described in this paper. A method of calibration was adopted using live traffic at the bridge site, based on which the accuracy of the system was determined. New methods of axle detection for bridge weigh-in-motion were investigated and verified in the field.

Application of current European standards (Eurocode) for the design of new bridges in assessment of existing ones proved non- efficient due to conservative assumptions regarding appliedload and subsequent response of these bridges. That is why decisions regarding existing bridges should be based on probabilistic approach, combining advanced methods of analysis with real values and data gathered with on-site measurement and laboratory testing. These types of data are gathered trough Structural Health Monitoring tools as it allows us to observe and record various data over a period of time in order to estimate bridge current condition and to track eventual changes in its behavior. Bridge Weigh-in-Motion measurements, as a part of structural health monitoring of existing bridges, provide us with detailed information regarding volume and weight of traffic on the bridge, while also tracking its structural response. Site specific load models for examined bridges can be developed from collected traffic data, using various extrapolation methods. Simple and fast, and yet precise method of traffic data processing is presented in first part of this paper, while its application, along with bridge structural response data, in optimized bridge assessment is presented in the second part of the paper.

Knowing realistic traffic loading is essential for optimised bridge assessment. A simplified approach to modelling the expected maximum traffic load effects, based on the convolution statistical method, has been proposed. Unlike the traditional method recommended by Moses and Verma (1987) it replaces the indirect calculation of characteristic load effects, through convoluted traffic loads (weights), with a procedure that convolutes the load effects (moments, forces) directly. The emphasis of the procedure is to evaluate accurately the tail of the load distribution histograms, the key factor for correct traffic load modelling, and then to apply a simple, yet sufficiently accurate procedure, applying cumulative distribution functions of the convoluted histograms, to forecast the realistic expected bending moments and shear forces of a bridge. Results of a concrete example, if compared to some alternative procedures, demonstrate that while the computing time is several times faster than for the traditional simulation methods, the results are comparable and conservative in most cases.

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. Deposited by bulk import

The European Union has expanded significantly in recent years. Sustainable trade within the Union leading to economic growth to the benefit of the 'old' and 'new' member states is thus extremely important. The road infrastructure is strategic and vital to such development since an uneven transport infrastructure, in terms of capacity and condition, has the potential to reinforce uneven development trends and hinder economic convergence of old and new member states. Significantly, in the decades since their design and construction, loading conditions have significantly changed for many major highway infrastructure elements/networks due primarily to increased freight volumes and vehicle sizes. This coupled with the gradual deterioration of a significant number of highway structures, due to their age, and the absence of a pan-European assessment framework can be expected to affect the smooth functioning of the infrastructure in its as-built condition, through increased periods of reduced flow due to planned and unplanned interventions for repair/rehabilitation. This paper reports the findings of a survey regarding the current status of the highway infrastructure elements in six countries within the European Union as reported by the owners/operators. The countries surveyed include a cross section of ‘existing’ older countries and ‘new’ accession countries. The current situations for bridges, culverts, tunnels and retaining walls are reported along with their potential replacement costs. The findings act as a departure point for further studies in support of a Centralized and/or Synchronised EU approach to Infrastructure Maintenance Management. Information in the form presented in this paper is central to any future decision making frameworks in terms of trade route choice and operations, monetary investment, optimized maintenance, management and rehabilitation of the built infrastructure and the economic integration of the newly joined member states. Deposited by bulk import

Simple numerical models of point loads are used to represent single and multiple vehicle events on two-lane bridges with a good road profile. While such models are insufficiently complex to calculate dynamic amplification accurately, they are presented here to provide an understanding of the influence of speed and distance between vehicles on the bridge dynamic response. Critical combinations of speed as a function of main bridge natural frequency and meeting point of two vehicles travelling in opposite directions are identified. It is proposed that such simple models can be used to estimate the pattern of critical speeds versus dynamic amplification of the bridge response for trucks on a relatively smooth surface. The crossing of a three-dimensional spring-dashpot truck is simulated over a bridge plate model to test this hypothesis for a range of road roughness. Further validation is carried out using the site-specific mean pattern associated to field measurements of bridge strains when traversed by a truck population. The latter is found to be closely resembled by the theoretical pattern derived from simple point load models. © 2010. This is the authors’ accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

When formulating an approach to assess bridge traffic loading with allowance for Vehicle-Bridge Interaction (VBI), a trade-off is necessary between the limited accuracy and computational demands of numerical models and the limited time periods for which experimental data is available. Numerical modelling can simulate sufficient numbers of loading scenarios to determine characteristic total load effects, including an allowance for VBI. However, simulating VBI for years of traffic is computationally expensive, often excessively so. Furthermore, there are a great many uncertainties associated with numerical models such as the road surface profile and the model parameter values (e.g., spring stiffnesses) for the heavy vehicle fleet. On site measurement of total load effect, including the influence of VBI, overcomes many of these uncertainties as measurements are the result of actual loading scenarios as they occur on the bridge. However, it is often impractical to monitor bridges for extended periods of time which raises questions about the accuracy of calculated characteristic load effects. Soft Load Testing, as opposed to Proof Load or Diagnostic Load Testing, is the direct measurement of load effects on bridges subject to random traffic. This paper considers the influence of measurement periods on the accuracy of soft load testing predictions of characteristic load effects, including VBI, for bridges with two lanes of opposing traffic. It concludes that, even for relatively short time periods, the estimates are reasonably accurate and tend to be conservative. Provided the data is representative, Soft Load Testing is shown to be a useful tool for calculating characteristic total load effect. Other funder European 6th Framework Project ARCHES (Assessment and Rehabilitation of Central European Highway Structures) au, ke - AS 04/11/2010

Bridge weigh-in-motion systems are based on the measurement of strain on a bridge and the use of the measurements to estimate the static weights of passing traffic loads. Traditionally, commercial systems employ a static algorithm and use the bridge influence line to infer static axle weights. This paper describes the experimental testing of an algorithm based on moving force identification theory. In this approach the bridge is dynamically modeled using the finite element method and an eigenvalue reduction technique is employed to reduce the dimension of the system. The inverse problem of finding the applied forces from measured responses is then formulated as a least squares problem with Tikhonov regularization. The optimal regularization parameter is solved using the L-curve method. Finally, the static axle loads, impact factors and truck frequencies are obtained from a complete time history of the identified moving forces. Deposited by bulk import

Many authors, using both experimental tests and complex numerical models, have examined the effect of vehicle velocity on a highway bridge’s dynamic amplification. Although these tests and models give valuable quantitative information on dynamic amplification, they give little insight into how amplification is affected by individual vehicle/bridge parameters. This paper uses relatively simple numerical models to investigate the effect of vehicle velocity on a bridge’s dynamic amplification. A single vehicle crossing a simply supported bridge is modeled as a constant point force. A set of critical velocities are determined associated with peaks of dynamic amplification for all beams. The reasons for these large amplifications are discussed. A more complex finite element model, validated with field tests, is used to test the applicability of the conclusions obtained from the simple models to a realistic bridge/vehicle system. Not applicable Link to published version - http://cedb.asce.org/cgi/WWWdisplay.cgi?150857. DG 14/07/10 au,ti,da,ke.kpw22/7/10

The dynamic component of bridge traffic loading is commonly taken into account with a Dynamic Amplification Factor (DAF) – the ratio between the maximum dynamic and static load effects on a bridge. In the design codes, this factor is generally higher than in reality. While this is fine for new bridges that must account for various risks during their life-time, it imposes unnecessary conservativism into assessment of the existing well defined bridges. Therefore, analysis of existing bridges should apply more realistic DAF values. One way of obtaining them experimentally is by bridge weigh-in-motion (B-WIM) measurements, which use an existing instrumented bridge or culvert to weigh all crossing vehicles at highway speeds. The B-WIM system had been equipped with two methods of obtaining an approximation to the static response of the. The first method uses the sum of influence lines. This method relies on accurate axle identification, the failure of which can have a large influence on the DAF value. The other method uses a pre-determined low-pass filter to remove the dynamic component of the measured signal; however an expert is needed to set the filter parameters. A new approach that tries to eliminate these two drawbacks has been developed. In this approach the parameters for the filter are determined automatically by fitting the filtered response to the sum of the influence lines. The measurement of DAF on a typical bridge site agrees with experiments performed in the ARCHES [1] project: dynamic amplification decreases as static loading increases.

Traffic load models available in building standards are most often developed for short or medium span bridges, however, it is necessary to develop traffic load models just for long span bridges, because the most unfavourable traffic situations are different. Weigh-in-Motion system data from highway A1 and A3 were used in this study. Measurement errors from data were cleaned using two groups of filters. The first group was based on vehicle validity codes recorded by both systems, if any circumstances might have influenced the measurements, the second group cleaned data using general filters for all vehicles and specific filters for trucks and cars. Additionally, vehicles were adjusted for influence of temperature. Data cleaning increased the average gross vehicle, so it could be considered as a conservative choice. Six traffic scenarios, each with different percentage of cars in the traffic, were made to assess the difference in loads from different traffic compositions. Traffic loads for long-span bridges were calculated using two approaches: the first assuming constant span length, the second, using influence lines from a bridge currently in design stage. Gumbel distribution were fitted to the calculate loads and they were extrapolated to probability of exceedance of 5% in 50 year period. Results show that influence line approach yield larger loads than those from constant-span. Both approaches result in loads larger than ones in Eurocode 1 Load Model 1, however, increase might have been caused by an increase in vehicle weight.