Live load model for highway bridges

Load models are developed for highway bridges. The models are based on the available statistical data on dead load, truck loads and dynamic loads. The paper deals mostly with the static live load. The model is derived from truck surveys, weight-in-motion measurements and other observations. Simple span moments, shears and negative moments are calculated for various spans. Extreme 75 year loads are determined by extrapolation. The important parameters also include girder distribution factors and multiple presence (more than one truck on the bridge). Multiple presence is considered in lane and side-by-side with various degrees of correlation between truck weights. The maximum load is calculated by simulations. The developed live model served as a basis for the development of new design provisions in the United States (LRFD AASHTO) and Canada (Ontario Highway Bridge Design Code).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30400/1/0000020.pd

A New Congested Traffic Load Model for Highway Bridges

Long span highway bridges are critical components of any nation’s infrastructure. Therefore accurate assessment of highway bridge loading is essential, and it is well known that congested traffic governs load effect for such bridges. Current congestion models use conservative assumptions about traffic and inter-vehicle gaps. This research investigates congested traffic flow through the use of traffic microsimulation which has the ability to reproduce complex traffic phenomena based on driver interactions. A time series model has been developed to produce a speed time-series similar to the results of the microsimulation. The speed time-series from the new model, combined with the established speed-gap relationship from the microsimulation, form the basis of a more computationally efficient congested traffic model. It is shown that the new model replicates aspects of microsimulation traffic well. However, the resulting load effects do not match as well as expected, and so further development of the model is required

Study of a fatigue load model for highway bridges in Southeast China

Oštećenja kostrukcija mostova uslijed zamora postala su jednim od glavnih uzroka oštećenja, ali još uvijek nisu utvrđeni standardni modeli vozila za proračun zamora mostova na autocestama u Kini. Oslanjajući se na WIM sustav, prikupljeni su podaci o opterećenju različitih vrsta vozila iz šest pokrajina u jugoistočnoj Kini. Za izračunavanje ekvivalentnog osovinskog opterećenja, ekvivalentnog međuosovinskog razmaka i omjera doprinosa zamora usvojeni su Minerov kriterij za kumulativno oštećenje zbog zamora i ekvivalentan kriterij amplitude naprezanja. Utvrđen je spektar opterećenja za zamor koristeći pet ekvivalentnih modela vozila.Although fatigue damage has become one of the main causes of structural damage to bridges, standard vehicle models for highway bridge fatigue design have not as yet been determined in China. Relying on the WIM system, load data were collected for different types of vehicles from six provinces in Southeast China. The Miner’s criterion for cumulative fatigue damage, and the equivalent stress amplitude criterion, were adopted for calculating the equivalent axle load, the equivalent wheelbase, and the fatigue damage contribution ratio. The fatigue load spectrum, including five equivalent vehicle models, was identified

Modeling Extreme Traffic Loading On Bridges Using Kernel Density Estimators

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 approac

Management Strategies for Special Permit Vehicles for Bridge Loading

An examination of weigh-in-motion 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 permits issued by the responsible authorities. It can be seen from the measurements that most of them are travelling at normal speeds. Photographic evidence indicates that, while many are accompanied by an escort vehicle, normal traffic is flowing alongside in other lanes. 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 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

Highway Bridge Traffic Loading

In this chapter, traditional approaches and recent advances in highway bridge traffic loading are described, which are of great significance for structural safety assessment of bridges. Indeed, it is widely accepted that consideration of site-specific traffic features can enable significant savings in maintenance operations. While short spans are governed by free-flowing traffic plus an allowance for the dynamic effects, long spans are governed by congested conditions. For the former, a promising research trend is the investigation of the dynamic vehicle-bridge interaction, which is shown to lead to dynamic effects much lower than previously thought. For the latter, advances in traffic flow modelling enable the simulation of realistic congestion patterns based on widely available free-flowing traffic data, thus partially overcoming a long-standing shortage of congestion data. Here, emphasis is given to the promising application of traffic microsimulation to long-span bridge loading, combined with a probabilistic approach based on the extreme value theory, to compute site-specific characteristic loading values

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 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 maximum lifetime. 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, vehicle gaps and 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. The results show that the patterns of correlation in the observed traffic have a small but significant effect on bridge loading

A case study in quantifying the economic benefit of a live strain monitoring campaign for a short-span pre-stressed concrete bridge

Peer reviewedPublisher PD

Finding the Distribution of Bridge Lifetime Load Effect by Predictive Likelihood

To assess the safety of an existing bridge, the loads to which it may be subject in its lifetime are required. Statistical analysis is used to extrapolate a sample of load effect values from the simulation period to the required design period. Complex statistical methods are often used and the end result is usually a single value of characteristic load effect. Such a deterministic result is at odds with the underlying stochastic nature of the problem. In this paper, predictive likelihood is shown to be a method by which the distribution of the lifetime extreme load effect may be determined. A basic application to the prediction of lifetime Gross vehicle Weight (GVW) is given. Results are also presented for some cases of bridge loading, compared to a return period approach and important differences are identified. The implications for the assessment of existing bridges are discussed

The influence of correlation on the extreme traffic loading of bridges

Accurate traffic loading models based on measured data are essential for the accurate assessment of existing bridges. There are well-established methods for the Monte Carlo simulation of single lanes of 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. This paper analyses traffic patterns using multi-lane WIM data collected at two European sites. It describes an approach to the Monte Carlo simulation of this traffic which applies variable bandwidth kernel density estimators to empirical traffic patterns of vehicle weights, gaps and speeds. This method provides a good match with measured data for multi-truck bridge loading events, and it is shown that correlation has a small but significant effect on lifetime maximum load effects