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 effect in bridges subject to random traffic. This paper considers the influence of measurement period 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.

Publication information Engineering Structures, 44 (44): 13-22Publisher Elsevier Item record/more information http://hdl.handle.net/10197/4858 Publisher's statementThis is the author's version of a work that was accepted for publication in Engineering Structures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Structures (44, , (2012) AbstractMoving 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 threedimensional vehicle model, allowing for Vehicle Bridge Interaction (VBI).

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 for heavy trucks on a bridge with a relatively smooth surface. The crossing of a threedimensional 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 due to the passage of a truck population. The latter is found to be closely resembled by the theoretical pattern derived from simple point load models. Keywords

Abstract.A method is presented of measuring a bridge's characteristic allowance for dynamic interaction, in the form of Assessment Dynamic Ratio (ADR). Using a Bridge-Weigh-in-Motion (Bridge WIM) system, measurements were taken at a bridge in Slovenia over a 58-day period. From the total observed traffic population, 5-axle trucks were extracted and studied. The Bridge WIM system inferred the static weights of the trucks, giving each measured event's dynamic increment of load. Theoretical simulations were carried out using a 3-dimensional vehicle model coupled with a bridge plate model, simulating a traffic population similar to the population measured at the site. These theoretical simulations varied those properties of the 5-axle fleet that influence the dynamic response; simulating multiple sets of total (dynamic + static) responses for a single measured static strain response. Extrapolating the results of these theoretical simulations to a 50-year ADR gives similar results to those obtained by extrapolating the data measured using the Bridge WIM system. A study of the effect of Bridge WIM system errors on the predictions of ADR is conducted, identifying a trend in the Bridge WIM calculations of maximum static response. The result of this bias is in turn quantified in the context of predicting characteristic maximum total load effect.

No 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.

The most commonly used program for the analysis of piles under static lateral loading is LPILE. The program uses the nonlinear Winkler springs recommended by the American Petroleum Institute (API) to model soil–pile interaction. The p–y (load–displacement) curves were developed from field tests, with pile diameters in the range 0.324–0.67 m. When these p–y curves are used to analyze load tests on piles with larger diameters, the computed load–deflection curves underestimate the stiffnesses of the test piles. This effect is referred to as the pile diameter effect. In this technical note, a very different approach is presented to evaluate the pile diameter effect. Both LPILE and a continuum-based finite element program VERSAT-P3D were calibrated to closely simulate the results of two lateral load tests on small-diameter piles at two different sites. VERSAT-P3D modelled the volume of the pile and LPILE did not. Each program was used to develop p–y curves for increasingly larger pile diameters up to 2.0 m. An important finding for practice is that there was no pile diameter effect for displacements up to 60 mm. LPILE can be used with confidence in practice in this displacement range. Thereafter, the load–deflection curves from LPILE became softer and the pile diameter effect became evident.

No abstract