Now showing 1 - 10 of 27
  • Publication
    Estimating characteristic bridge traffic load effects using Bayesian statistics
    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
    The Effect of Lane Changing on Long-Span Highway Bridge Traffic Loading
    Maximum loading on long-span bridges typically occurs in congested traffic conditions. As traffic becomes congested car drivers may change lane, increasing the tendency for trucks to travel in platoons. For long-span bridges this phenomenon may increase the regularity and severity of bridge repair programs, with potential significant associated costs. This research investigates the effect of lane changing by car drivers on bridge loading. A Monte Carlo simulation model in which individual car drivers probabilistically decide, based on a lane-changing bias probability, whether or not to change lane has been developed. The sensitivity of bridge loading to this factor is investigated for different bridge lengths and traffic compositions. This research concludes that the lane-changing behavior of car drivers has an effect on bridge loading for long-span bridges, and the magnitude of this effect is quite sensitive to the percentage of trucks in the traffic.
  • Publication
    Estimation of lifetime maximum distributions of bridge traffic load effects
    This paper considers the problem of assessing traffic loading on road bridges. A database of European WIM data is used to determine accurate annual maximum distributions of load effect. These in turn are used to find the probability of failure for a number of load effects. Using the probability of failure as the benchmark, traditional measures of safety – factor of safety and reliability index – are reviewed. Both are found to give inconsistent results, i.e., a given factor of safety or reliability index actually corresponds to a range of different probabilities of failure
  • 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
    Site Specific Modelling of Traffic Loading on Highway Bridges
    Accurate traffic loading models based on measured weigh-in-motion (WIM) data are essential for the accurate assessment of existing bridges. Much work has been published on the Monte Carlo simulation of single lanes of heavy vehicle traffic, and this can easily be extended to model the loading on bridges with two independent streams of traffic in opposing directions. However, a typical highway bridge will have multiple lanes in the same direction, and various types of correlation are evident in measured traffic, such as groups of very heavy vehicles travelling together and heavy vehicles being overtaken by lighter ones. These traffic patterns affect the probability and magnitude of “multiple presence” loading events on bridges, and are significant for the maximum lifetime loading on the bridge. This paper analyses traffic patterns using multi-lane WIM data collected at four European sites. It describes an approach to the Monte Carlo simulation of this traffic which seeks to replicate the observed patterns of vehicle weights, same-lane and interlane gaps, and vehicle speeds by applying variable bandwidth kernel density estimators to empirical traffic patterns. This allows the observed correlation structure to be accurately simulated but also allows for unobserved patterns to be simulated. The process has been optimised so as to make it possible to simulate traffic loading on bridges over periods of 1,000 years or more, and this removes much of the variability associated with estimating characteristic maximum load effects from shorter periods of either measured or simulated data. The results of this analysis show that the patterns of correlation in the observed traffic have a small but significant effect on bridge loading.
  • Publication
    Management Strategies for Special Permit Vehicles for Bridge Loading
    An examination of weigh-in-motion (WIM) data collected recently at sites in five European countries has shown that vehicles with weights well in excess of the normal legal limits are found on a daily basis. These vehicles would be expected to have special permits issued by the responsible authorities. It can be seen from the WIM measurements that most of them are travelling at normal highway speeds (around 80 km/h). Photographic evidence indicates that, while many are accompanied by an escort vehicle in front and/or behind, normal traffic is flowing alongside in other lanes of the highway. As European freight volume grows, the frequency of these special vehicles can be expected to increase. Hence, the probability of them meeting a heavy truck travelling in the opposite direction on a bridge also increases. Gross vehicle weights in excess of 100 t have been observed at all sites, and are a daily occurrence in the Netherlands. Most of these extremely heavy vehicles are either mobile cranes or low loaders carrying construction equipment. Both types have multiple axles at very close spacing, and the gross weight and axle layout have implications for bridge loading. This paper presents findings based on a simulation model which incorporates the load effects for all observed truck types on short to medium span bridges. It is evident that special vehicles govern the lifetime maximum bridge loading, and the occurrence of extremely heavy trucks is sufficiently frequent that meeting events can be expected during the design lives of the bridges. The effects of different management strategies for special permit vehicles are modelled and the results are presented.
  • 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
    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
    Non-Linear Response of Structures to Characteristic Loading Scenarios
    To assess the safety of an existing bridge, the traffic loads to which it may be subjected in its lifetime need to be accurately quantified. In this paper the 75 year characteristic maximum traffic load effects are found using a carefully calibrated traffic load simulation model. To generate the bridge loading scenarios, an extensive weigh in motion (WIM) database, from three different European countries, is used. Statistical distributions for vehicle weights, inter-vehicle gaps and other characteristics are derived from the measurements, and are used as the basis for Monte Carlo simulations of traffic representing many years. An advantage of this “long-run” simulation approach is that it provides information on typical extreme traffic loading scenarios. This makes possible a series of nonlinear finite element analyses of a reinforced concrete bridge to determine the response to typical characteristic maximum loadings.
  • Publication
    Probabilistic study of lifetime load effect distribution of bridges
    Assessment of highway bridge safety requires a prediction of the probability of occurrence of extreme load effects during the remaining life of the structure. While the assessment of the strength of an existing bridge is relatively well understood, the traffic loading it is subject to, has received less attention in the literature. The recorded traffic data are often limited to a number of days or weeks due to the cost of data collection. Studies in the literature have used many different methods to predict the lifetime maximum bridge load effect using a small amount of data, including fitting block maximum results to a Weibull distribution and raising maximum daily or maximum weekly distributions to an appropriate power. Two examples are used in this study to show the importance of the quantity of data in predicting the lifetime maximum distribution. In the first, a simple example is used for which the exact theoretical probabilities are available. Hence, the errors in estimations can be assessed directly. In the second, ‘long-run’ simulations are used to generate a very large database of load effects from which very accurate estimates can be deduced of lifetime maximum effects. Results are presented for bidirectional traffic, with one lane in each direction, based on Weigh-in-Motion data from the Netherlands.