Site-Specific Extra-Heavy Truck Load Characteristics and Bridge Safety Assessment

Han, Wanshui; Liu, Xiaodong; Gao, Guangzhong; Xie, Qiang; Yuan, Yangguang

doi:10.1061/(asce)as.1943-5525.0000917

Cited by 20 publications

(19 citation statements)

References 23 publications

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“…e investigation focuses on the FVDs' influence on the bridge dynamic responses under traffic flow; thus the key variables are FVD parameters while the traffic flow needs to remain unchanged. Monitored traffic data gathered by weigh-in-motion system are processed to form typical traffic load to obtain the response of the studied structure [32]. e data collection site lies in Zhangjiawan bridge at the G104 highway in Zhejiang province of China, which is a coastal developed area.…”

Section: Traffic Flow Simulation Based Onmentioning

confidence: 99%

“…First, the monitored data are statically initialized to separate vehicles in carriageway and passing lane according to the statistical property of lane distribution. As the statistical analysis in hours can reflect the randomness and the peak hours of traffic flow, hourly traffic flow is often used in traffic load analysis [32,33]. e WIM data were divided into 24 hours each day and the total vehicle weight of each period was calculated.…”

Section: Traffic Flow Simulation Based Onmentioning

confidence: 99%

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Influence of Fluid Viscous Damper on the Dynamic Response of Suspension Bridge under Random Traffic Load

Zhao

Huang

Long

et al. 2020

Advances in Civil Engineering

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Fluid viscous dampers (FVDs) are widely used in long-span suspension bridges for earthquake resistance. To analyze efficiently the influences of FVDs on the dynamic response of a suspension bridge under high-intensity traffic flow, a bridge-vehicle coupling method optimized by isoparametric mapping and improved binary search in this work was first developed and validated. Afterwards, the traffic flow was simulated on the basis of monitored weigh-in-motion data. The dynamic responses of bridge were analyzed by the proposed method under different FVD parameters. Results showed that FVDs could positively affect bridge dynamic response under traffic flow. The maximum accumulative longitudinal girder displacement, longitudinal girder displacement, and longitudinal pylon acceleration decreased substantially, whereas the midspan girder bending moment, pylon bending moment, longitudinal pylon displacement, and suspender force were less affected. The control efficiency of maximum longitudinal girder displacement and accumulative girder displacement reached 33.67% and 57.71%, longitudinal pylon acceleration and girder bending moment reached 31.51% and 7.14%, and the pylon longitudinal displacement, pylon bending moment, and suspender force were less than 3%. The increased damping coefficient and decreased velocity exponent can reduce the bridge dynamic response. However, when the velocity exponent was 0.1, an excessive damping coefficient brought little improvement and may lead to high-intensity work under traffic flow, which will adversely affect component durability. The benefits of low velocity exponent also reduced when the damping coefficient was high enough, so if the velocity exponent has to be increased, the damping coefficient can be enlarged to fit with the velocity exponent. The installation of FVDs influences dynamic responses of bridge structures in daily operations and this issue warrants investigation. Thus, traffic load should be considered in FVD design because structural responses are perceptibly influenced by FVD parameters.

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Section: Traffic Flow Simulation Based Onmentioning

confidence: 99%

Section: Traffic Flow Simulation Based Onmentioning

confidence: 99%

Influence of Fluid Viscous Damper on the Dynamic Response of Suspension Bridge under Random Traffic Load

Zhao

Huang

Long

et al. 2020

Advances in Civil Engineering

Self Cite

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“…The existing identification methods can be divided into two categories: indirect methods based on structural response [7][8][9] and direct methods based on weighing sensor measurement. [10][11][12] The most representative indirect method is the theory of moving force identification (MFI), which originated from a series of classic MFI algorithms proposed by Chan et al in the 1990s, including the interpretive method I (IMI), 13 the interpretive method II (IMII), 14 the time domain method (TDM), 15 and the frequency-TDM (FTDM). 16 Since then, many improvements to these four methods have also been conducted.…”

Section: Introductionmentioning

confidence: 99%

“…Obrien and Enright 10 established a load model after analyzing the characteristics of vehicle loads measured by WIMs and used Monte Carlo simulation to predict the aggressiveness of traffic for bridge loading. Han et al 12 statistically studied key characteristics of extra heavy trucks (EHTs) on two typical routes of China, as well as applied EHT load cases in a bridge–vehicle simulation to assess the bending capacities of small‐ and medium‐span bridges. Under the assumption that the load effect conforms to a stationary Gaussian process, the aforementioned studies extrapolate load effects using WIM measurements and attempt to evaluate the long‐term performance of bridge structures by extrapolated data.…”

Section: Introductionmentioning

confidence: 99%

An accurate and robust monitoring method of full‐bridge traffic load distribution based on YOLO‐v3 machine vision

Dan

2020

Struct Control Health Monit

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The accurate and stable identification of the traffic load distribution on the bridge deck is of great significance to bridge health monitoring and safety early warning. To accomplish this task, we have combined the weigh-in-motion system (WIMs) with machine vision and developed a traffic load monitoring (TLM) technology for the whole bridge deck. For bridge health monitoring, the TLM should be available for online structural analysis, have high accuracy, and be able to adapt to changes in lighting conditions. However, existing TLM methods are difficult to meet the requirements of real-time, accuracy, and lighting robustness simultaneously. In this regard, this paper proposes an improved full-bridge TLM method based on YOLO-v3 convolutional neural network. The core of this method includes training a dual-target detection model and correcting vehicle locations. The detection model can identify profiles of the entire vehicle and its tail and can mark them with compact rectangular boxes. Based on the corner points of these rectangular boxes, an optical geometry model is proposed to measure vehicle dimensions and correct vehicle centroids, thereby the vehicle locations can be estimated more accurately. By applying the time synchronization of cameras and the WIMs, each measured load is paired with the vehicle "pixel cluster" detected in the video; further, the traffic load distribution on the whole bridge deck is identified accurately in real-time. Verified by the field data of a ramp bridge, the proposed method is proved more accurately on the identification of vehicle locations, more robust lighting adaptability, and faster calculation speed, which can meet the requirements of field monitoring of traffic load distribution.

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“…Wang et al (2018c) present a new approach for determining the truck weight limit of simply supported steel girder bridges based on the fatigue reliability of bridge girders under the action of random traffic flows considering the presence of multiple trucks. Han et al (2018a) analyze the site-specific load characteristics of extra-heavy trucks (EHTs) based on a weigh-in-motion (WIM) data measure. Li et al (2019) studied the dynamic performance of a strengthened concrete-filled steel tubular (CFST) arch bridge that has experienced serious damage from long-term heavy-truck action.…”

mentioning

confidence: 99%

Damage Detection and Condition Assessment of Civil Structures

2020

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Site-Specific Extra-Heavy Truck Load Characteristics and Bridge Safety Assessment

Cited by 20 publications

References 23 publications

Influence of Fluid Viscous Damper on the Dynamic Response of Suspension Bridge under Random Traffic Load

Influence of Fluid Viscous Damper on the Dynamic Response of Suspension Bridge under Random Traffic Load

An accurate and robust monitoring method of full‐bridge traffic load distribution based on YOLO‐v3 machine vision

Damage Detection and Condition Assessment of Civil Structures

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