Now showing 1 - 10 of 27
  • Publication
    Modelling of Highway Bridge Traffic Loading: Some Recent Advances
    The accurate estimation of site-specific lifetime extreme traffic load effects is an important element in the cost-effective assessment of bridges. In recent years, the improved quality and increasing use of weigh-in-motion technology has resulted in better quality and larger databases of vehicle weights. This has enabled measurements of the regular occurrence of extremely heavy vehicles, with weights in excess of 100 t. The collected measurements have been used as the basis for building and calibrating a Monte Carlo simulation model for bridge loading. The computer programs written to implement this model generate simulated traffic in two lanes – for both the same direction and the bidirectional cases – and calculate load effects for bridges of various spans. The research focuses on free-flowing traffic on short to medium-span bridges. This paper summarizes recent advances and their contribution to the highway bridge traffic loading problem.
  • Publication
    Modelling Extreme Traffic Loading on Bridges Using Kernal Density Estimators
    (Eugenides Foundation, 2011-10-13) ; ;
    Kernel density estimators are a non-parametric method of estimating the probability density function of sample data. In this paper, the method is applied to find characteristic maximum daily truck weights on highway bridges. The results are then compared with the conventional approach.
  • Publication
    Portable Bridge WIM Data Collection Strategy for Secondary Roads
    A common method of collecting traffic loading data across a large road network is to use a network of permanent pavement-based WIM systems. An alternative is to use one or more portable Bridge Weigh-In-Motion systems which are moved periodically between bridges on the network. To make optimum use of such a system, a suitable data collection strategy is needed to choose locations for the system. This paper describes a number of possible strategies which the authors have investigated for the National Roads Authority in Ireland. The different strategies are examined and their advantages and disadvantages compared. Their effectiveness at detecting a heavy loading event is also investigated and the preferred approach is identified.
  • Publication
    Spatially Variable Assessment of Lifetime Maximum Load Effect Distribution in Bridges
    Bridge structures are key components of highway infrastructure and their safety is clearly of great importance. Safety assessment of highway bridges requires accurate prediction of the extreme load effects, taking account of spatial variability through the bridge width and length. This concept of spatial variability i s also known as random field analysis. Reliability - based bridge assessment permits the inclusion of uncertainty in all parameters and models associated with the deterioration process. Random field analysis takes account of the probability that two points n ear each other on a bridge will have correlated properties. This method incorporates spatial variability which results in a more accurate reliability assessm ent. This paper presents an integrated model for spatial reliability analysis of reinforced concre te bridges that considers both the bridge capacity and traffic load. A sophisticated simulation model of two - directional traffic is used to determine accurate annual maximum distributions of load effect. To generate the bridge loading scenarios, an extensi ve Weigh-in-Motion (WIM) database, from five European countries, is used. For this, statistical distributions for vehicle weights, inter - vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for a Monte Carlo simulation of traffic. Results are presented for bidirectional traffic, with one lane in each direction, with a total flow of approximately 2000 trucks per day.
  • Publication
    A Bayesian approach for estimating characteristic bridge traffic load effects
    This paper investigates the use of Bayesian updating to improve estimates of characteristic bridge traffic loading. Over recent years the use Weigh-In-Motion technologies has increased hugely. Large Weigh-In-Motion databases are now available for multiple sites on many road networks. The objective of this work is to use data gathered throughout a road network to improve site-specific estimates of bridge loading at a specific Weigh-In-Motion site on the network. Bayesian updating is a mathematical framework for combining prior knowledge with new sample data. The approach is applied here to bridge loading using a database of 81.6 million truck records, gathered at 19 sites in the US. The database represents the prior knowledge of loading throughout the road network and a new site on the network is simulated. The Bayesian approach is compared with a non-Bayesian approach, which uses only the site-specific data, and the results compared. It is found that the Bayesian approach significantly improves the accuracy of estimates of 75-year loading and, in particular, considerably reduces the standard deviation of the error. With the proposed approach less site-specific WIM data is required to obtain an accurate estimate of loading. This is particularly useful where there is concern over an existing bridge and accurate estimates of loading are required as a matter of urgency.
  • Publication
    Estimating Characteristic Bridge Loads On A Non-Primary Road Network
    When collecting truck loading data on a primary road network a common, approach is to install a large network of permanent pavement based Weigh-In-Motion systems. An alternative to this approach would be to use one or more portable Bridge Weigh-In-Motion systems which could be moved between bridges at regular intervals to determine the traffic loading throughout the network. A data collection strategy is needed to put such a system to best use. This paper details the data collection strategies which were examined for the National Roads Authority in Ireland. The use of urban economic concepts including Central Place Theory are discussed as methods for analysing which roads are expected to experience the greatest truck loading.
  • Publication
    Using Weigh-in-Motion Data to Determine Aggressiveness of Traffic for Bridge Loading
    (American Society of Civil Engineers, 2013-03) ;
    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.
      890Scopus© Citations 79
  • Publication
    Monte Carlo simulation of extreme traffic loading on short and medium span bridges
    (Informa UK (Taylor & Francis), 2013-12) ;
    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.
      799Scopus© Citations 110
  • Publication
    Validation of Scenario Modelling for Bridge Loading
    (Technika, Vilnius Gediminas Technical University, 2016-09) ; ; ;
    Accurate estimates of characteristic bridge load effects are required for efficient design and assessment of bridges, and long-run traffic simulations are an effective method for estimating the effects. For multi-lane same-direction traffic, truck weights and locations on the bridge are correlated and this affects the calculated load effects. Scenario Modelling is a recently developed method which uses weigh-in-motion (WIM) data to simulate multi-lane same-direction traffic while maintaining location and weight correlations. It has been unclear however whether the method may produce unrealistic driver behaviour when extrapolating beyond the weigh-in-motion measuring period. As weigh-in-motion databases with more than about a year of data are not available, a microsimulation traffic model, which can simulate driver behaviour, is used here to assess the accuracy of extrapolating traffic effects using Scenario Modelling. The microsimulation is used to generate an extended reference dataset against which the results of Scenario Modelling are compared. It is found that the characteristic load effects obtained using Scenario Modelling compare well with the reference dataset. As a result, for the first time researchers and practitioners can model two-lane same-direction traffic loading on bridges while being confident that the approach is generating accurate estimates of characteristic load effects as well as effectively reproducing the complex traffic correlations involved.
      501Scopus© Citations 3
  • Publication
    Importance of the tail in truck weight modeling for bridge assessment
    (American Society of Civil Engineering (ASCE), 2010-03) ; ;
    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 weigh-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.
      913Scopus© Citations 59