Eugene J. O'Brien

The use of vehicle acceleration measurements to estimate road roughness

Road roughness is a broad term that incorporates everything from potholes and cracks to the random deviations that exist in a profile. To build a roughness index, road irregularities need to be measured first. Existing methods of gauging the roughness are based either on visual inspections or using one of a limited number of instrumented vehicles that can take physical measurements of the road irregularities. This paper proposes the collection of data from accelerometers fixed in a specific vehicle type and the use of this data to estimate the road condition. Although the estimate is approximate, accelerometers are being increasingly used by car manufacturers to improve suspension performance and the proposed method is relatively inexpensive to implement and provide road managers with constantly updated measurements of roughness. This approach is possible due to the relationship between the power spectral densities of road surface and vehicle accelerations via a transfer function. This paper shows how road profiles can be accurately classified using axle and body accelerations from a range of simulated vehicle–road dynamic scenarios.

Monte Carlo Simulation of Extreme Traffic Loading on Short and Medium Span Bridges

The accurate estimation of site-specific lifetime extreme traffic load effects is an important element in the cost-effective assessment of bridges. A common approach is to use statistical distributions derived from weigh-in-motion measurements as the basis for Monte Carlo simulation of traffic loading. However, results are highly sensitive to the assumptions made, not just with regard to vehicle weights but also to axle configurations and gaps between vehicles. This paper presents a comprehensive model for Monte Carlo simulation of bridge loading for free-flowing traffic and shows how the model matches results from measurements on five European highways. The model has been optimised to allow the simulation of many years of traffic and this greatly reduces the variance in calculating estimates for lifetime loading from the model. The approach described here does not remove the uncertainty inherent in estimating lifetime maximum loading from data collected over relatively short time periods.

Importance of the Tail in Truck Weight Modeling for Bridge Assessment

To predict characteristic extreme traffic load effects, simulations are sometimes performed of bridge loading events. To generalize the truck weight data, statistical distributions are fitted to histograms of weight measurements. This paper is based on extensive weight-in-motion measurements from two European sites and shows the sensitivity of the characteristic traffic load effects to the fitting process. A semiparametric fitting procedure is proposed: direct use of the measured histogram where there are sufficient data for this to be reliable and parametric fitting to a statistical distribution in the tail region where there are less data. Calculated characteristic load effects are shown to be highly sensitive to the fit in the tail region of the histogram.

The use of a dynamic truck-trailer drive-by system to monitor bridge damping

Bridge structures are continuously subject to degradation due to the environment, ageing and excess loading. Periodic monitoring of bridges is therefore a key part of any maintenance strategy as it can give early warning if a bridge becomes unsafe. This article investigates an alternative method for the monitoring of bridge dynamic behaviour: a truck–trailer vehicle system, with accelerometers fitted to the axles of the trailer. The method aims to detect changes in the damping of a bridge, which may indicate the existence of damage. A simplified vehicle–bridge interaction model is used in theoretical simulations to assess the effectiveness of the method in detecting those changes. The influence of road profile roughness on the vehicle vibration is overcome by recording accelerations from both axles of a trailer and then analysing the spectra of the difference in the accelerations between the two axles. The effectiveness of the approach in detecting damage simulated as a loss in stiffness is also investigated. In addition, the sensitivity of the approach to the vehicle speed, road roughness class, bridge span length, changes in the equal axle properties and noise is investigated.

Comparison of two independently developed bridge weigh–in–motion systems

An experiment is described in which two independently developed bridge weigh–in–motion (WIM) systems are tested and compared, both for accuracy and durability. The systems, an Irish prototype still under development, and a commercially available American system, were tested on a bridge in Slovenia. Eleven statically pre–weighed trucks were each driven over the bridge several times at a range of typical highway speeds. Accuracies for axle and gross vehicle weights are presented within the framework of the draft European WIM specification, and the bias which can be introduced by the selection of calibration truck is demonstrated. Performance factors relating to durability are also discussed with particular emphasis on axle detectors.

Simplified site-specific traffic load models for bridge assessment

Traffic load is identified as one of the greatest sources of uncertainty in the assessment of bridges. In recent years, simulation techniques, using measured traffic data, have been used to predict the characteristic traffic load effects on bridges. However, the techniques are complex, sensitive to the assumptions adopted and require specialist statistical expertise. This work presents a simplified site-specific traffic load model that generates comparable load effects to the corresponding results from a full simulation. While the simplified model is still sensitive to the underlying assumptions, these can be carefully reviewed prior to the method being approved. Further, the simplified method can be employed by practising engineers for bridge assessment.

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

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.

A Review of Indirect Bridge Monitoring Using Passing Vehicles

Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice.

Using Weigh-in-Motion Data to Determine Aggressiveness of Traffic for Bridge Loading

This paper presents results based on the analysis of an extensive database of weigh-in-motion (WIM) data collected at five European highway sites in recent years. The data are used as the basis for a Monte Carlo simulation of bridge loading by two-lane traffic, both bidirectional and in the same direction. Long runs of the simulation model are used to calculate characteristic bridge load effects (bending moments and shear forces), and these characteristic values are compared with design values for bridges of different lengths as specified by the Eurocode for bridge traffic loading. Various indicators are tested as possible bases for a bridge aggressiveness index to characterize the traffic measured by the WIM data in terms of its influence on characteristic bridge load effects. WIM measurements can thus be used to determine the aggressiveness of traffic for bridges. The mean maximum weekly gross vehicle weight is proposed as the most effective of the indicators considered and is shown to be well correlated with a wide range of calculated characteristic load effects at each site.

Report of current studies performed on normal load model of EC1. Part 2. Traffic loads on bridges

ABSTRACT This report gives results of some new studies performed to validate the european road traffic load model proposed by the Eurocode EC1. Weight in motion has developed greatly in the last ten years and confidence in the accuracy of recorded data has increased significantly. Traffic data recently obtained from a number of representative European sites are used to re-calibrate the codified main load model of the European bridge loading code, Eurocode 1 Part 3. A wide range of real and virtual bridge forms were chosen for the study. Simulations were performed using free-flowing and jammed traffic. Load effects generated were determined and statistical extrapolations were performed, where appropriate, to determine characteristic values for the load effects. Some of the assumptions used in the derivation of the original loading model were re-assessed.