Dynamic Impact of Heavy Traffic Load on Typical T-Beam Bridges Based on WIM Data

Han, Wanshui; Yuan, Yangguang; Huang, Pingming; Wu, Jun; Wang, Tao; Liu, Huanju

doi:10.1061/(asce)cf.1943-5509.0000991

Cited by 21 publications

(11 citation statements)

References 23 publications

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“…e traffic data of G104 highway in Shaoxing, China, was monitored using the weigh-in-motion (WIM) equipment and the vehicle load characteristics including the classification of vehicle type were also investigated in previous research of the authors [26]. Here, the WIM data recorded in May 13, 2013, i.e., 5338 sets of vehicle data, is employed to analyze the DIF spectrum of PCT-30 under random traffic flow.…”

Section: Traffic Datamentioning

confidence: 99%

“…A local minimum value obviously appears in the 12 th time block for the potential reason that the corresponding time interval is just the lunch time. According to the previous research [26], the vehicle types of highway transportation can be merged into the following five categories:…”

Section: Traffic Datamentioning

confidence: 99%

“…e traffic loading scenarios are extracted according to the recorded velocity and arriving time of vehicles as well as the span length of the prototype bridge, in which all vehicles simultaneously loading on the prototype bridge are merged into one individual loading scenario [26]. Totally 4926 loading scenarios are generated, including 4514 single-vehicle loading scenarios and 412 double-vehicle loading scenarios.…”

Section: Random Traffic Flow Based On Wim Datamentioning

confidence: 99%

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Refined Vehicle‐Bridge Interaction Analysis Using Incompatible Solid Finite Element for Evaluating Stresses and Impact Factors

Xie

Han

Yuan

2020

Advances in Civil Engineering

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e vehicle-bridge interaction can induce bridge vibration and consequently fatigue, durability deterioration, local damage, and even collapse of bridge structure. In this paper, a solid vehicle-bridge interaction (VBI) analysis method is developed to provide refined analysis on the bridge responses including displacement and local stress under vehicle loads. e incompatible solid finite element (FE) is introduced to model the bridge, where the element shear locking is alleviated by incompatible displacement modes without sacrificing the computational efficiency. Benchmark example shows the incompatible solid element has superior computational efficiency compared to the conventional solid element. By virtue of the mass-spring-damper vehicle model, the interaction between vehicle and bridge is simulated with point-to-point contact assumption and the coupled dynamic equations are solved via nonlinear iteration. A case study on a simply supported T-girder bridge is conducted to validate the developed solid VBI analysis method and then the dynamic impact factor (DIF) of the bridge is evaluated based on the computed stress results and compared to code values. Results show that the solid VBI analysis method yields more accurate time-history bridge responses including displacement and stress under moving vehicles than the grillage method despite higher computational cost. Particularly, it can simulate realistic stress distribution and concentration along any concerned sections as well as in local components, which can provide detail information on the bridge behavior under dynamic loads. On the other hand, the DIF based on the computed stress result generally agrees well with the code values except for heavy vehicles where the stress-based DIF is slightly higher than the value in Chinese code while lower than that of AASHTO, suggesting the value specified by Chinese code may underestimate the DIF of heavy vehicles in certain circumstances to which more attention should be paid.

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Section: Traffic Datamentioning

confidence: 99%

Section: Traffic Datamentioning

confidence: 99%

Section: Random Traffic Flow Based On Wim Datamentioning

confidence: 99%

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Refined Vehicle‐Bridge Interaction Analysis Using Incompatible Solid Finite Element for Evaluating Stresses and Impact Factors

Xie

Han

Yuan

2020

Advances in Civil Engineering

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“…The traffic monitoring site is located at the Zhongjiawan viaduct of Shaoxing City, which is a segment of the G104 highway in China. The information of the viaduct and monitoring equipment was introduced by Han et al (2016).…”

Section: Traffic Monitoringmentioning

confidence: 99%

“…In addition, the adoption of 80 t will greatly reduce the amount of data used for analysis and increase the analysis efficiency which is also considerable in simulation analysis. Besides this, the truncating point of 80 t is sufficient to cover the adverse loading scenarios, which has also been used in the previous researches by the authors of this article (Han et al, 2015(Han et al, , 2016. Figure 1 shows the gross weight distribution of all monitored vehicle and the truncation process.…”

Section: Traffic Monitoringmentioning

confidence: 99%

Structure safety assessment under heavy traffic based on weigh in motion and simulation analysis

Yuan

Han

Huang

et al. 2017

Advances in Structural Engineering

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Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

Deng

Zhai

et al. 2022

Structural Contr & Hlth

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Deflection is an important issue in bridge structural health monitoring. An accurate deflection-vehicle load-temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflectionvehicle load-temperature correlation, the correlation modeling method needs further studies. A novel deflection-vehicle load-temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time-continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established.Taking a suspension bridge in China as an example, the prediction accuracy of short-, medium-, and long-term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature-and vehicle-induced deflection components is proposed. The results show that the predicted deflection of the short-term correlation model is basically consistent with the real-time monitoring data, while the medium-and long-term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature-and vehicle-induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment.

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Dynamic Impact of Heavy Traffic Load on Typical T-Beam Bridges Based on WIM Data

Cited by 21 publications

References 23 publications

Refined Vehicle‐Bridge Interaction Analysis Using Incompatible Solid Finite Element for Evaluating Stresses and Impact Factors

Refined Vehicle‐Bridge Interaction Analysis Using Incompatible Solid Finite Element for Evaluating Stresses and Impact Factors

Structure safety assessment under heavy traffic based on weigh in motion and simulation analysis

Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

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