Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges

Abstract

Fatigue load models for road bridges given in the Eurocode EN1991-2 have been calibrated considering real traffic measurements that became available around 1990. Since then, traffic composition has evolved considerably, also considering the issuing of the 96/53/EC Directive, which legitimated member states, on an equal and not discriminatory basis, to allow the circulation of Long and Heavy Vehicles (LHVs). Thus, the appropriateness of fatigue load models to cover also the effects of these vehicles, which are longer, heavier and potentially more damaging than common Heavy Goods Vehicles (HGVs), became an issue. The aim of the study is to assess how the evolution of European traffic influences the fatigue assessment of bridges. To capture the essence of the problem, three different real traffic measurements are compared in terms of fatigue damage: the Auxerre (FR) traffic, adopted to define fatigue load models in EN1991-2; the Moerdijk (NL) traffic, characterized by a high percentage of LHVs; and the Igualada (ES) traffic. To assess the current relevance of fatigue load models LM2 and LM4 of EN1991-2, the aptitude of these models to adequately reproduce the effects caused by LHVs is discussed in detail. The results demonstrate that the Auxerre traffic is still the most onerous; that the Moerdijk traffic is generally more severe than the Igualada traffic, and that the fatigue load models of Eurocode do not require major updates. The study is further supplemented by investigating the suitability of the formulae provided in the Eurocodes for the damage equivalence factors λ2 and λ3 to express the influence of the total lorry volume on the fatigue damage. In that latter case, the conclusion is that the formulae proposed in the Eurocodes, based on the assumption of a linear fatigue strength S–N curve with constant conventional slope m, could lead to erroneous, even unsafe, estimates of the fatigue life, especially when details are characterized by constant amplitude fatigue limit ΔσD, thus calling for further improvements of the formulae themselves.

Impact of Traffic Load Randomness on Fatigue of Steel Bridges

“…The road traffic randomness pertains not only to the types of vehicles (their loads, number of axles, total length, axle spacing), but also to the manner in which they move across a bridge (O'Brien, & Enright, 2011). When describing traffic on road bridges, the following aspects can be considered significant (Croce, 2019(Croce, , 2020Liu, Xiao, Lu, & Deng, 2016;Song, & Ding, 2013;Wenzel, & Veit-Egerer, 2009;Yan, Deng, & He, 2016):…”

“…It is also used to determine maximum loads of structures under assumption that vehicles have identical dimensions and weight (Harman, et al 1983;Moses, et al 1975). Attempts to describe the results of field tests of road bridges in terms of this process have also been made (Croce, 2020;Wysokowski, 2018;Zhang, et al, 2017).…”

Innovative Carbon-Doped Composite Pavements with Sensing Capability and Low Environmental Impact for Multifunctional Infrastructures

“…Measuring traffic loads on road infrastructures can be useful for improving the accuracy of fatigue estimation of critical structures, in particular bridges [1][2][3][4][5], that are prone to traffic-induced fatigue accumulation that could lead to sudden failure [6]. Weigh-inmotion (WIM) sensors [7][8][9] can be used to measure the type and magnitude of traffic loads, providing key data on daily usage and on effects of extraordinary events and improving condition-based maintenance decisions [10].…”

Weibull S-N Fatigue Strength Curve Analysis for A572 Gr. 50 Steel, Based on the True Stress—True Strain Approach

“…50 steel material, a probabilistic S-N curve does not exist. The main goal of the S-N curves analysis is related to the strength material behavior and its correlation with the defined stress ratio [1][2][3][4]. Structural elements are subjected to a range of stress values, so to determine the probabilistic S-N curve, any material steel is necessary [1][2][3]5,6].…”

“…Therefore, by applying the proposed methodology, µ x and σ x are both unique, so they can be used to determine the corresponding S-N percentile for any other material where σ 1 and σ 2 are known. However, it is important to note that, if the proposed method is going to be used to determine the probabilistic percentiles for another material, we must first be sure the used σ 1 and σ 2 stresses values are those values that generate the failure in the material, or equivalent if the used data is a failure testing data [4,6]. On the other hand, it is important to highlight that, according to the Canadian standard CSA G40.21, the A572 grade 50 steel is somewhat equivalent to the CSA G40.21 Gr.…”