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

Study of Same-lane and Inter-lane GVW Correlation

Extensive work has been done over the last two decades on the simulation of traffic loading on bridges. The methodology used is to generate a number of years of simulated traffic and to use extreme value statistics to predict more accurately the characteristic loading for a given bridge. The parameters and probability distributions used in the Monte Carlo simulation must be based on observed sample traffic data. Some previous studies have made unsubstantiated assumptions regarding correlation between the Gross Vehicle Weights (GVW) of trucks in the same lane, or between trucks in adjacent, same direction lanes. For this paper, an extensive database of Dutch Weigh-in-Motion data is analysed. Data are collected from two same-direction lanes and are time-stamped to the nearest 0.01 seconds. The statistical characteristics of this set of data are presentd, and various techniques are used to establish the nature and extend of GVW correlation

ecocomDP: A flexible data design pattern for ecological community survey data

The idea of harmonizing data is not new. Decades of amassing data in databases according to community standards - both locally and globally - have been more successful for some research domains than others. It is particularly difficult to harmonize data across studies where sampling protocols vary greatly and complex environmental conditions need to be understood to apply analytical methods correctly. However, a body of longterm ecological community observations is increasingly becoming publicly available and has been used in important studies. Here, we discuss an approach to preparing harmonized community survey data by an environmental data repository, in collaboration with a national observatory. The workflow framework and repository infrastructure are used to create a decentralized, asynchronous model to reformat data without altering original data through cleaning or aggregation, while retaining metadata about sampling methods and provenance, and enabling programmatic data access. This approach does not create another data ‘silo’ but will allow the repository to contribute subsets of available data to a variety of different analysis-ready data preparation efforts. With certain limitations (e.g., changes to the sampling protocol over time), data updates and downstream processing may be completely automated. In addition to supporting reuse of community observation data by synthesis science, a goal for this harmonization and workflow effort is to contribute these datasets to the Global Biodiversity Information Facility (GBIF) to increase the data’s discovery and use

Statistical Computation for Extreme Bridge Traffic Load Effects

The maintenance of highway infrastructure constitutes a major expenditure in many countries. This cost can be reduced significantly by minimizing the repair or replacement of highway bridges. In the assessment of existing bridges, the strength estimate tends to be more accurate than that of traffic loading, due to the more variable nature of loading. Recent advances in the statistical analysis of highway bridge traffic loading have resulted in more accurate forecasts of the actual loading to which a bridge is subject. While these advances require extensive numerical computation, they can significantly improve the accuracy of the calculation. This paper outlines the recent advances and describes the associated computational aspects in detail

Headway modelling for traffic load assessment of short to medium span bridges

Site-specific assessment of the loading to which existing bridges are subject has considerable potential for saving on rehabilitation and replacement costs of the bridge stock. Monte Carlo simulations, with traffic measurements from site, are used to estimate the characteristic values for load effects. In this paper, it is shown that the critical loading events from which the characteristic effects are derived, are strongly dependent on the assumptions used for the headways of successive trucks. A new approach which uses measured headway statistical distributions is developed and is shown to be a reasonable balance between conservative assumptions and less realistic scenarios. The sensitivity of characteristic load effects to conventional headway assumptions is shown to be significant.European Research CouncilPublisher version - http://www.istructe.org/thestructuralengineer/abstract.asp?pid=5494. DG 09/07/10 ti, ke - TS 22.07.1

Implications of Future Heavier Trucks for Europe\u27s Bridges

European road freight transport has increased by 38% between 1995 and 2005 and this strong growth trajectory seems likely to continue into the future. To address this growth without compromising the competitativeness of European transport, some countries are contemplating the introduction of longer and heavier trucks, with up to 8 axles and gross weights of up to 60 t. This has the advantage of reducing the number of vehicles for a given volume or mass of freights, reducing labour, fuel and other costs. However, many roads authorities are concerned about the implications for Europe\u27s bridge infrastructure. For bridge loading, it is the combination of gross weight and truck length that determine load effects such as bending moment and shear force in the deck. A probabilistic analysis is required to assess whether these proposed trucks will lead to greater maximum lifetime (characteristic) load effects. If this was found to be the case, it would necessitate the strengthening of a great number of vulnerable bridges throughout the continent or it could even prevent the introduction of heavier trucks. This paper reviews the factors governing traffic loading on short/medium span bridges. There is considerable conservatism in the Eurocode traffic loading model. Hence, bridges designed to this or similar modern codes of practice can be shown to be safe in the presence of significant numbers of longer and heavier trucks. Even more significantly, using data from one of Europe\u27s most heavily trafficked highways, it is shown that the critical loading events are often special permit trucks such as cranes or low-loaders with up to 12 axles. Hence, characterisitic load effects (bending moments etc.) are unlikely to be strongly influenced by the most common truck type- 5-axle articulated trucks- and are therefore unlikely to be affected by the introduction of longer and heavier versions of them

Study of Same-lane and Inter-lane GVW Correlation

Extensive work has been done over the last two decades on the simulation of traffic loading on bridges. The methodology used is to generate a number of years of simulated traffic and to use extreme value statistics to predict more accurately the characteristic loading for a given bridge. The parameters and probability distributions used in the Monte Carlo simulation must be based on observed sample traffic data. Some previous studies have made unsubstantiated assumptions regarding correlation between the Gross Vehicle Weights (GVW) of trucks in the same lane, or between trucks in adjacent, same direction lanes. For this paper, an extensive database of Dutch Weigh-in-Motion data is analysed. Data are collected from two same-direction lanes and are time-stamped to the nearest 0.01 seconds. The statistical characteristics of this set of data are presentd, and various techniques are used to establish the nature and extend of GVW correlation

Effect of Single-Lane Congestions on Long-Span Bridge Traffic Loading

Bridge and Concrete Research in Ireland, Dublin, Ireland, 6 - 7 September, 2012It is well known that traffic loading of long-span bridges is governed by congestion. In spite of the fact that field observations in the past decades have shown that congestion can take up different forms, most previous studies on bridge traffic loading consider only a queue of standstill vehicles. In this paper, a micro-simulation tool is used for generating congested traffic on a single-lane roadway. The underlying micro-simulation model has been found capable of successfully replicating observed congestion patterns on motorways by simulating single-lane traffic with identical vehicles. Here trucks are introduced into the model, in an investigation of the total load for a 200m span bridge. Different congestion patterns are found and studied in relation to their effect on loading. It is found that the bumper-to-bumper queue is not necessarily the most critical situation for the sample long-span bridge, since it does not allow the flowing of vehicles and therefore decreases the probability of observing critical loading events. Slow-moving traffic, corresponding to heavy congestion, can be more critical, depending on the truck proportion.European Commission - Seventh Framework Programme (FP7