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Reliability-Based Analysis and Life-Cycle Management of Load Tests
Published in Eva O.L. Lantsoght, Load Testing of Bridges, 2019
Dan M. Frangopol, David Y. Yang, Eva O. L. Lantsoght, Raphaël D. J. M. Steenbergen
InFigure 9.13, the complementary cumulative distribution function of the daily maxima of the traffic load effects is shown; it has been generated using the influence lines of the bending moment in the midspan of the bridge. Weigh-in-motion (WIM) data is used to sample the traffic flow. The datapoints give the empirical distribution function; the continuous line is the fitted analytical distribution function which is used in the full probabilistic analysis for the determination of the proof loading value. The WIM measurements result from a bridge subjected to 2.5 million trucks per year. Since the Halvemaans Bridge is subjected to only 51,500 trucks per year, this difference is corrected for in the distribution function of the traffic load effect. Statistical uncertainty was included to account for the uncertainty in the extrapolated part of the distribution function.
Impact of overloaded vehicles on load equivalency factors and service period of flexible pavements
Published in Andreas Loizos, Imad L. Al-Qadi, A. (Tom) Scarpas, Bearing Capacity of Roads, Railways and Airfields, 2017
D. Rys, J. Judycki, J. Jaskula
The Weigh in Motion (WIM) system allows to improve vehicle control and it is developing intensively in Europe (Jacob and Loo, 2008). The WIM stations are installed in order to preselect overloaded vehicles and to collect statistical data. Currently in the UE countries overloaded vehicles must be weighing again on the static, legalized weights to impose the fine. However there are some trials to improve WIM systems measurement precision and to solve the legislation problems to enable usage of WIM systems for automatic identifying and imposing the fine for overloaded vehicles (Burnos and Gajda 2016, Oskarbski and Kaszubowski 2016, Doupal et al., 2008, Burnos et.al, 2007).
Overturning mechanisms and evaluation strategy of box girder bridges under extreme vehicle load
Published in Hiroshi Yokota, Dan M. Frangopol, Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, 2021
Z.J. Zhou, H.Y. Wu, X.F. Shi, H.Y. Ma
llegal over-loaded vehicles on road should be monitored and managed before they harm bridges. Nowadays, the weight in motion (WIM) system has been a mature technology to get traffic information (B. Enright et al., 2013). Combined with traffic control system, traffic monitoring, and control system (TMCS) can monitor and stop heavy trucks in time, reducing the risk of bridge collapse under extreme traffic conditions.
Reliability assessment of existing concrete bridges under the passage of heavy trucks considering bending–shear interaction
Published in Structure and Infrastructure Engineering, 2023
Junyong Zhou, Cuimin Hu, Junping Zhang, Tao Li, Mingguan Yang
In the previous static weighing approaches, overloaded trucks would attempt to bypass the inspection, rendering the overload data collected incomplete. The development of weigh-in-motion (WIM) technology enables the accurate measurement of vehicle weight without interfering with the traffic, affording comprehensive information on overloaded trucks (Karim, Ibrahim, Saifizul, & Yamanaka, 2014). With these overloaded truck data, many studies have focused on the precise modeling of truck weights, such as multimodal finite mixture modeling (Zhou, Shi, Caprani, & Ruan, 2018) and semi-parametric modeling (Huang et al., 2019; O’Brien, Enright, & Getachew, 2010). In more recent studies, overloaded trucks have been separated from normal vehicles and analyzed in terms of their impact on bridge performance (Fiorillo & Ghosn, 2018; Han, Wu, Cai, & Chen, 2015). These studies demonstrate the adverse effects of overloaded trucks on the reliability and maintenance costs of infrastructural assets; however, only the single-mechanism failure mode (for example, bending or shear failure) of bridges has been investigated. For girder bridges undertaking service traffic loadings, bending and shear are strongly correlated in terms of both the load effect and structural resistance.
Bridge performance indicators based on traffic load monitoring
Published in Structure and Infrastructure Engineering, 2019
Ana Mandić Ivanković, Dominik Skokandić, Aleš Žnidarič, Maja Kreslin
Weigh-in-motion (WIM) (Moses, 1979; Žnidarič, Kreslin, & Kalin, 2016) is a procedure that is used to measure axle load and gross weight of a vehicle as it drives over a measurement site at full speed, without the need for slowing down or stopping. Two types of WIM systems exist: one with the sensors built into the pavement and the other involving the bridge weigh-in-motion (B-WIM). The second one uses instrumented bridges as weighing scales (Moses, 1979). The main advantage of the B-WIM systems over the pavement ones is that they are portable. Furthermore, as all sensors are installed under the bridge, the installation and maintenance works do not impede the flow of traffic (Žnidarič et al., 2012). Finally, they provide supplementary information about bridge behaviour under traffic load, which is highly valuable for structural analysis.
Comparison of vehicle re-identification models for trucks based on axle spacing measurements
Published in Journal of Intelligent Transportation Systems, 2018
Gulsevi Basar, Mecit Cetin, Andrew P. Nichols
Most transportation agencies rely on point detectors (e.g., inductive loops, axle detectors) located at specific locations on highways to collect data on traffic volumes, vehicle classes, and other relevant attributes of vehicles and traffic. By utilizing disaggregate vehicle data, researchers developed vehicle re-identification (VRI) algorithms to match measurements at two data collection sites that belong to the same vehicle. This enables tracking the movement of individual vehicles between different sites anonymously, which in turn provides valuable information for the estimation of travel times, the detection of congestion and travel delays, and origin–destination (OD) flows. In addition, when vehicles, in particular trucks, are re-identified between two weigh-in-motion (WIM) sites, the matched axle-weight measurements can also be used for calibration of WIM sensors. WIM technology has been used by many transportation agencies since 1980s in order to collect data on the attributes of heavy vehicles on freeways while vehicles travel at normal highway speeds (Chatterjee, Liao, & Davis, 2017; Nichols & Cetin, 2015; Shin-Ting & Lianyu, 2015). Data collection process in a typical WIM is enabled by the use of several sensors such as piezoelectric, bending plate, and load cells (Shin-Ting & Lianyu, 2015).