Bernard Enright

Monte Carlo Simulation of Extreme Traffic Loading on Short and Medium Span Bridges

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.

Importance of the Tail in Truck Weight Modeling for Bridge Assessment

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 weight-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.

Using Weigh-in-Motion Data to Determine Aggressiveness of Traffic for Bridge Loading

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.

Microsimulation Evaluation of Eurocode Load Model for American Long-Span Bridges

The ability to accurately predict traffic loading is essential for cost-effective bridge maintenance and repair programs. The traffic load model currently used in the United States for the design of long-span bridges was developed over three decades ago. In the meantime, vehicle characteristics and traffic patterns have changed. The Eurocode for traffic loading is more recent, but was calibrated only for bridges up to 200 m long. In this work, weigh-in-motion traffic records from 11 different sites across Alabama are used to establish congested traffic loading. Traffic microsimulation is used to generate congestion based on real traffic data. Influence lines for two typical long-span bridges, one cable-stayed bridge, and one suspension bridge are determined using finite-element models. These are used in the microsimulation model to estimate the bridge-load effects caused by congested traffic. These results are extrapolated to find the characteristic lifetime maximum values that are used to evaluate the Eurocode load model to assess its suitability for long-span bridges. In a similar way, the current American load model for long-span bridges, commonly known as the ASCE model, is evaluated to see if it accurately reflects the congested traffic loading that is currently found on American highways. Recent research has suggested the use of the AASHTO HL-93 load model to estimate the effects of traffic loading on long-span bridges, and this model is also evaluated in this work.

Spatial time-dependent reliability analysis of reinforced concrete slab bridges subject to realistic traffic loading

Abstract Resistance and loads are often correlated in time and space. The paper assesses the influence of these correlations on structural reliability/probability of failure for a typical two-lane reinforced concrete (RC) slab bridge under realistic traffic loading. Spatial variables for structural resistance are cover and concrete compressive strength, which in turn affect the strength and chloride-induced corrosion of RC elements. Random variables include pit depth and model error. Correlation of weights between trucks in adjacent lanes and inter-vehicle gaps are also included and are calibrated against weigh-in-motion data. Reliability analysis of deteriorating bridges needs to incorporate uncertainties associated with parameters governing the deterioration process and loading. One of the major unanswered questions in the work carried out to date is the influence of spatial variability of load and resistance on failure probability. Spatial variability research carried out to date has been mainly focused on predicting the remaining lifetime of a corroding structure and spatial variability of material, dimensional and environmental properties. A major shortcoming in the work carried out to date is the lack of an allowance for the spatial variability of applied traffic loads. In this article, a two-dimensional (2D) random field is developed where load effects and time-dependent structural resistance are calculated for each segment in the field. The 2D spatial time-dependent reliability analysis of an RC slab bridge found that a spatially correlated resistance results in only a small increase in probability of failure. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small.

Review of HL-93 Bridge Traffic Load Model Using an Extensive WIM Database

Abstract HL-93, the current bridge traffic load model used in the United States is examined here. Weigh-in-motion (WIM) data from 17 sites in 16 states containing 74 million truck records are used to assess the level of consistency in the characteristic load effects (LEs) implied by the HL-93 model. The LEs of positive and negative bending moments and shear force are considered on single- and two-lane same-direction slab and girder bridges with a range of spans. It is found that the ratio of WIM-implied LE to HL-93 LE varies considerably from one LE to another. An alternative model is proposed that achieves improvements in consistency in this ratio for the LEs examined, especially for the single-lane case. The proposed model consists of a uniformly distributed load whose intensity varies with bridge length.

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.

Implications of Future Heavier Trucks for Europe's 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 competitiveness 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 freight, reducing labour, fuel and other costs. However, many roads authorities are concerned about the implications for Europes 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 Europes 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, characteristic 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.

The Effect of Truck Permitting Policy on US Bridge Loading

Accurate estimates of characteristic traffic load effects are greatly beneficial in prioritizing bridges for repair and replacement. The extreme loading events likely to cause characteristic load effects are dominated by very heavy permit trucks. As these trucks are significantly heavier and are subject to stricter controls than standard trucks, they may be treated separately from the general truck population. This paper examines truck loading at 3 Weigh-in-Motion (WIM) sites in the United States and develops filtering rules to identify permit trucks based on the available axle spacing information. Once all trucks have been classified, permit and standard trucks are examined separately to get a better understanding of their importance for bridge loading. A Monte Carlo simulation model is developed which allows permit trucks to be simulated independently of the standard truck population. The truck simulation model is used to investigate the changes in characteristic load effects resulting from changes in permit-issuing policy. normal trucks and routine permits, and special design vehicles, which are trucks above the limits for routine permits that require individual analysis. It can be argued that standard vehicles are not well controlled and should have a higher factor of safety or return period. Permit vehicles, on the other hand, are subject to a greater degree of control which may justify a lesser factor of safety or return period. Previous work has shown that characteristic load effects are caused predominantly by permit vehicles (Enright & OBrien, 2012). In this paper, WIM data from three states in the United States are filtered to separate apparent permit vehicles from standard vehicles. The two data subsets – apparent standard and apparent permit – are examined separately. 2. WEIGH-IN-MOTION DATABASE For this study, data from three WIM sites in the United States is analyzed. This WIM data has been collected as part of a follow-on project of the Federal Highway Administration’s Long Term Pavement Performance (LTPP) program for traffic data collection. In the early years of the LTPP, traffic data was collected with inconsistent quality control measures (Walker & Cebon 2012). A plan was developed in 1999 under which, among other things, quality control was improved and implemented centrally. This led to a significant improvement in WIM data reliability. Since 2003, ‘research quality’ WIM data is being collected at 28 of the Specific Pavement Studies LTPP sites. Research Quality is, for this purpose, defined as 210 days of data per year of known calibration, meeting LTPP’s accuracy requirements for steering and tandem axles, gross vehicle weight, vehicle length, speed, and axle spacing. The recommended WIM technologies include bending plate, load cell, and quartz sensors. The three sites used here all belong to this group of research-quality WIM sites. Table 1 shows the details of the WIM sites used in this work. At all sites, only one lane in one direction is measured, that being the slow lane. All data was collected between 1 st January 2008 and 31 st December 2011. Table 1. Details of WIM sites Site Road Weekdays of Data Average Trucks/day Arizona I-10 East 996 4988 Illinois I-57 North 1008 3139 Indiana US-93 North 87

NONLINEAR RESPONSE OF STRUCTURES TO CHARACTERISTIC LOADING SCENARIOS

EMI/PMC Joint Conference of the Engineering Mechanics Instiatue and the 11th ASCE Joint Specialty Conference on Probalistic Mechanics and Structural Reliability, June 17-20, 2012, Notre Dame, USA