Influence of road surface roughness on dynamic impact factor of bridge by full-scale dynamic testing

2005 ◽  
Vol 32 (5) ◽  
pp. 825-829 ◽  
Author(s):  
Young Suk Park ◽  
Dong Ku Shin ◽  
Tae Ju Chung
Keyword(s):  
Surface Roughness ◽  
Impact Factor ◽  
Road Surface ◽  
Roughness Coefficient ◽  
International Roughness Index ◽  
The Road ◽  
Surface Profiles ◽  
Road Surface Roughness ◽  
Dynamic Impact Factor ◽  
The Impact

Effects of road surface roughness on the dynamic impact factor of bridge are investigated through full-scale field loading tests under controlled traffic conditions. The dynamic time histories of displacements are obtained for twenty-five bridges on Korean highways. The impact factors of the bridges are evaluated by using the measured displacements. The road surface profiles of the twenty-five bridges are also measured at every 10 to 30 cm interval in the span direction. By using the measured road surface profiles, the international roughness index (IRI) and the roughness coefficients of the bridges are evaluated. The linear regression and correlation analyses are performed to obtain the coherences between the IRI and the roughness coefficient and between the IRI and the impact factor. The sample correlation coefficients between the impact factor and the IRI and between the impact factor and the roughness coefficient are calculated to be 0.61 and 0.62, respectively, showing a strong coherence between the road surface roughness and the impact factor.Key words: bridge, impact factor, road surface roughness, international roughness index, roughness coefficient.

A Computerized Method for Dynamic Response of Vehicle-Bridge Interaction System under Various Conditions

2013 ◽  
Vol 639-640 ◽  
pp. 1214-1219
Author(s):  
Yao Xiao ◽  
Zheng Qing Chen ◽  
Xu Gang Hua
Keyword(s):  
Surface Roughness ◽  
Dynamic Effect ◽  
Integrated System ◽  
Road Surface ◽  
Dynamic Responses ◽  
Superposition Method ◽  
The Road ◽  
Computerized Method ◽  
Road Surface Roughness ◽  
The Impact

A computerized method is presented for computing the dynamic responses of bridges under moving vehicles. The bridge and vehicle are treated as integrated system and modal superposition method is applied to transfer the equation of motion into modal coordinate system. The road roughness/unevenness is also considered. The effects of different vehicle models, vehicle passing speed and road surface roughness on bridge dynamic responses are studied. The impact factor representing the dynamic effect of passing vehicle is calculated for different road surface roughness

Vehicle-Bridge Coupled Vibration on Concrete-Filled Steel Tubular Arch Bridge

2011 ◽  
Vol 90-93 ◽  
pp. 1106-1111
Author(s):  
Hong Xia Tan ◽  
Zheng Qing Chen
Keyword(s):  
Impact Factor ◽  
Cross Sections ◽  
Dynamic Characteristics ◽  
Impact Factors ◽  
Arch Bridge ◽  
The Road ◽  
Moving Vehicles ◽  
Cfst Arch Bridge ◽  
Road Surface Roughness ◽  
The Impact

This paper studies the dynamic response and the impact factor of the concrete-filled steel tubular (CFST) arch bridge named Hejiang River Bridge under the moving vehicles. Research shows that the impact factor of CFST arch bridge at the vault and 1/4 arch rib is greatly influenced by the road surface roughness (RSR), and it is increased with the grade of RSR increases, meanwhile it is different at apiece section position of the arch bridge. The impact factor doesn't vary monotonically with the speed of vehicle, it appears the maximum when the speed of vehicle is between 20-25 km/h and 35-50 km/h, and the impact factors of different cross-sections are not just the same with the changing regularity of speed. Therefore, the dynamic characteristics of different structural components should be calculated in designing CFST arch bridge for discrepant dynamic characteristics of various constructional elements.

scholarly journals A Frequency Compensation Algorithm of Four-Wheel Coherence Random Road

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Feng ◽  
Xinjie Zhang ◽  
Konghui Guo ◽  
Fangwu Ma ◽  
Hamid Reza Karimi
Keyword(s):  
Surface Roughness ◽  
Coherence Function ◽  
Road Surface ◽  
Domain Model ◽  
Important Influence ◽  
Frequency Compensation ◽  
The Road ◽  
Compensation Algorithm ◽  
Left And Right ◽  
Road Surface Roughness

The road surface roughness is the main source of kinematic excitation of a moving vehicle, which has an important influence on vehicle performance. In recent decades, random road models have been widely studied, and a four-wheel random road time domain model is usually generated based on the correlation of the four-wheel input, in which a coherence function is used to describe the coherence of the road input between the left and right wheels usually. However, during our research, there are some conditions that show that the road PSD (power spectral density) of one wheel is smaller than the other one on the same axle. Actually, it is caused by the uncorrelation between the left- and right-wheel road surface roughness. Hence, a frequency compensation algorithm is proposed to correct the deviation of the PSD of the road input between two wheels on the same axle, and it is installed in a 7-DOF vehicle dynamic study. The simulation result demonstrates the applicability of the proposed algorithm such that two-wheel road input deviation compensation has an important influence on vehicle performances, and it can be used for a control system installed in the vehicle to compensate road roughness for damper tuning in the future.

A Simple Approach for Simulating the Road Surface Roughness Involved in Vehicle-Bridge Interaction Systems

2018 ◽  
Vol 18 (07) ◽  
pp. 1871009 ◽  
Author(s):  
Yao Zhang ◽  
Hai Sheng Zhao ◽  
Seng Tjhen Lie
Keyword(s):  
Surface Roughness ◽  
General Purpose ◽  
Simple Approach ◽  
Road Surface ◽  
The Other ◽  
Fe Modeling ◽  
The Road ◽  
Road Surface Roughness ◽  
External Forces ◽  
Small Elements

This paper presents an idea for modeling the road surface roughness in a vehicle–bridge interaction (VBI) system, by simulating it equivalently as two external forces each acting on the two subsystems of vehicle and bridge. Such an idea can be easily included in general-purpose commercial finite element (FE) software like ABAQUS and ANSYS. Compared with frequently used coupled and uncoupled FE models, the present approach is more convenient, since it does not require any self-developed FE codes. The other advantage is that it does not require very small elements in the FE modeling, as is the case with conventional approaches for simulating the irregularity in the profile of road surface roughness, which may be computationally inefficient.

scholarly journals A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter

2020 ◽  
Vol 20 (10) ◽  
pp. 2042006
Author(s):  
Jiantao Li ◽  
Xinqun Zhu ◽  
Siu-Seong Law ◽  
Bijan Samali
Keyword(s):  
Surface Roughness ◽  
Kalman Filter ◽  
Damage Detection ◽  
Interaction Force ◽  
Road Surface ◽  
Dynamic Responses ◽  
The Road ◽  
Stiffness Reduction ◽  
Bridge Damage ◽  
Road Surface Roughness

Drive-by bridge inspection using acceleration responses of a passing vehicle has great potential for bridge structural health monitoring. It is, however, known that the road surface roughness is a big challenge for the practical application of this indirect approach. This paper presents a new two-step method for the bridge damage identification from only the dynamic responses of a passing vehicle without the road surface roughness information. A state-space equation of the vehicle model is derived based on the Newmark-[Formula: see text] method. In the first step, the road surface roughness is estimated from the dynamic responses of a passing vehicle using the dual Kalman filter (DKF). In the second step, the bridge damage is identified based on the interaction force sensitivity analysis with Tikhonov regularization. A vehicle–bridge interaction model with a wireless monitoring system has been built in the laboratory. Experimental investigation has been carried out for the interaction force and bridge surface roughness identification. Results show that the proposed method is effective and reliable to identify the interaction force and bridge surface roughness. Numerical simulations have also been conducted to study the effectiveness of the proposed method for bridge damage detection. The vehicle is modeled as a 4-degrees-of-freedom half-car and the bridge is modeled as a simply-supported beam. The local bridge damage is simulated as an elemental flexural stiffness reduction. Effects of measurement noise, surface roughness and vehicle speed on the identification are discussed.The results show that the proposed drive-by inspection strategy is efficient and accurate for a quick review on the bridge conditions.

scholarly journals Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

2019 ◽  
Vol 9 (21) ◽  
pp. 4715 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Binh Thai Pham ◽  
Le Hoang Son ◽  
Nguyen Trung Thang ◽  
Hai-Bang Ly ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hybrid Models ◽  
Road Surface ◽  
International Roughness Index ◽  
Adaptive Network ◽  
Inference System ◽  
Roughness Index ◽  
Road Surface Roughness

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

Damage Detection of Bridges Using Response of Vehicle Considering Road Surface Roughness

2015 ◽  
Vol 15 (03) ◽  
pp. 1450057 ◽  
Author(s):  
Zhenhu Li ◽  
Francis Tat Kwong Au
Keyword(s):  
Surface Roughness ◽  
Numerical Study ◽  
Road Surface ◽  
The Road ◽  
Damage Location ◽  
Girder Bridges ◽  
Optimal Value ◽  
Global Optimal ◽  
Vertical Accelerations ◽  
Road Surface Roughness

This paper presents a genetic algorithm (GA)-based method to identify the damage of girder bridges from the response of a vehicle moving over the bridge. The continuous wavelet transform-based method works when the surface is smooth but the identification becomes difficult when the road surface is rough. To deal with this problem, the identification process is formulated as an optimization problem and a guided GA is used to search for the global optimal value. The vertical accelerations of the vehicle running over the bridge at the intact and damaged states are used to identify the occurrence and location of the damage. Frequencies of the bridge at the intact and damaged states can be extracted from these responses, from which the frequency-based method can roughly estimate the possible locations of the damage. These locations are not unique as frequencies alone are insufficient to identify the damage location. However these initial results can be used to narrow down the search region on which the GA can focus. Numerical study shows that the strategy can identify the damage location for simply supported and continuous girder bridges even though road surface roughness and measurement noise are taken into account.

scholarly journals Laboratory experiment on the nano-TiO2 photocatalytic degradation effect of road surface oil pollution

2020 ◽  
Vol 9 (1) ◽  
pp. 922-933
Author(s):  
Qing’e Wang ◽  
Kai Zheng ◽  
Huanan Yu ◽  
Luwei Zhao ◽  
Xuan Zhu ◽  
...  
Keyword(s):  
Photocatalytic Degradation ◽  
Oil Pollution ◽  
Road Surface ◽  
Test Method ◽  
Driving Safety ◽  
The Road ◽  
Road Surfaces ◽  
The Many ◽  
The Impact ◽  
Degradation Effect

AbstractOil leak from vehicles is one of the most common pollution types of the road. The spilled oil could be retained on the surface and spread in the air voids of the road, which results in a decrease in the friction coefficient of the road, affects driving safety, and causes damage to pavement materials over time. Photocatalytic degradation through nano-TiO2 is a safe, long-lasting, and sustainable technology among the many methods for treating oil contamination on road surfaces. In this study, the nano-TiO2 photocatalytic degradation effect of road surface oil pollution was evaluated through the lab experiment. First, a glass dish was used as a substrate to determine the basic working condition of the test; then, a test method considering the impact of different oil erosion degrees was proposed to eliminate the effect of oil erosion on asphalt pavement and leakage on cement pavement, which led to the development of a lab test method for the nano-TiO2 photocatalytic degradation effect of oil pollution on different road surfaces.

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