• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.111-123
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• 2006

In the design of bridges, exact evaluation of traffic loading is very important for the safety and maintenance of bridges. In general, traffic loading is represented by live load (including impact load) and fatigue load. For exact evaluation of traffic loading, it is important to get reliable and comprehensive truck data including the traffic and weight information. It requires the development of Bridge Weigh-In-Motion (BWIM), which measures the truck weights without stopping the traffic. Objectives of the study is (1) to develop the BWIM system, (2) to verified the system in bridges in Highways.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.143-153
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• 2006

In design of bridges, estimation of actions and loadings is very important for the safety and maintenance of bridges. In general, effect of traffic loading on the bridge can be modeled as live load (including impact load) and fatigue load. For estimation of traffic loading, it is important to get reliable and comprehensive truck statistical data such as the traffic and weight information. To get statistical data, Bridge Weigh-In-Motion (BWIM), which measures the truck weights without stopping the traffic, is need to be developed. In this study, BWIM system with various functions is developed first. Then this system is used to get comprehensive truck data. Traffic loadings including fatigue and live loading are formulated from the truck data acquired from the bridges. Objectives of this study are to develop the BWIM system, to apply the system in test bridge in Highway, and to formulate the live and fatigue loading for bridge design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.79-85
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• 2011

Bridge Weigh-in-Motion(BWIM) system calculates a travelling vehicle's weight without interruption of traffic flow by analyzing the signals that are acquired from various sensors installed in the bridge. BWIM system or data accumulated from the BWIM system can be utilized to development of updated live load model for highway bridge design, fatigue load model for estimation of remaining life of bridges, etc. Field test with moving trucks including various load cases should be performed to guarantee successful development of precise BWIM system. In this paper, a numerical simulation technique is adopted as an alternative or supplement to the vehicle traveling test that is indispensible but expensive in time and budget. The constructed numerical model is validated by comparison experimentally measured signal with numerically generated signal. Also vehicles with various dynamic characteristics and travelling conditions are considered in numerical simulation to investigate the variation of bridge responses. Considered parameters in the numerical study are vehicle velocity, natural frequency of the vehicle, height of entry bump, and lateral position of the vehicle. By analyzing the results, it is revealed that the lateral position and natural frequency of the vehicle should be considered to increase precision of developing BWIM system. Since generation of vehicle travelling signal by the numerical simulation technique costs much less than field test, a large number of test parameters can effectively be considered to validate the developed BWIM algorithm. Also, when artificial neural network technique is applied, voluminous data set required for training and testing of the neural network can be prepared by numerical generation. Consequently, proposed numerical simulation technique may contribute to improve precision and performance of BWIM systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.671-678
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• 2020

In this paper, a wireless measurement system has been developed which can measure the deformation of a tire in real time by utilizing strain gauge sensors and Zigbee wireless communication. A strain gauge sensor is mounted inside the tire and then the strain on the tire is obtained using the DSP module. The acquired sensor values are transmitted into the vehicle by radio communication. The wireless receiver module installed inside the vehicle can monitor the deformation of the tire in real time. The deformation of the tire can be used for measuring the load applied to each tire or the speed of the tire. The load or speed applied to the tires are essential parameters for the stable control of autonomous vehicles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.831-838
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• 2006

An experimental/analytic study has been conducted to understand the adverse effects of low vehicle speed, high axle load and high tire pressure on the performance of asphalt pavements. Of 33 asphalt sections at KHC test road, two sections having different base layer thickness (180 mm versus 280 mm) are adopted for rollover tests. During the test, a standard three-axle dump truck maintains a steady state condition as moving along the wheel path of a passing lane, and lateral offsets and real travel speed are measured with a laser-based wandering system. Test results suggest that vehicle speed affects both longitudinal and transverse strains at the bottom of asphalt layer (290 mm and 390 mm below the surface), and even slightly influences the measured vertical stresses at the top of subbase and subgrade due to the dynamic effect of rolling vehicle. Since the anisotropic nature of asphalt-aggregate mixtures, the difference between longitudinal and transverse strains appears prominent throughout the measurements. As the thickness of asphalt pavement increases, the measured lateral strains become larger than its corresponding longitudinal strains. Over the limited testing conditions, it is concluded that higher axle weight and higher tire pressures induce more strains and vertical stresses, leading to a premature deterioration of pavements. Finally, a layered elastic analysis overestimates the maximum strains measured under the 1st axle load, while underestimating the maximum vertical stress in both pavement sections.

• International Journal of Highway Engineering
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• v.8 no.3 s.29
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• pp.129-141
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• 2006

The vertical soil pressure developed in the granular layer of asphalt pavement system is influenced by various factors, including the wheel load magnitude, the loading speed, and asphalt pavement temperature. This research observed the distribution of vertical soil pressure in pavement supporting layer by investigating measured data from soil pressure gage in the KHC Test Road. The existing specification of subbase and subgrade compaction was also evaluated with measured vertical pressure. The finite element analysis was conducted to verify the accuracy of results with measured data because it can maximize research capacity without significant field test. The test data was collected from A5, A7, A14, and A15 test sections at August, September, and November 2004 and August 2005. Those test sections and test data were selected because they had best quality. The size of influence area was evaluated and the vertical pressure variation was investigated with respect to load level, load speed, and pavement temperature. The lower speed, higher load level, and higher pavement temperature increased the vertical pressure and reduced the area of influence. The finite element result showed the similar trend of vertical pressure variation in comparison with measured data. The specification of compaction quality for subbase and subgrade is higher than the level of vertical pressure measured with truck load so that it should be lurker investigated.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.123-132
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• 2011

The purpose of this study is to calculate and propose rational multi-lane loading factors for bridge design considering the probability of simultaneous truck passing in adjacent lanes and real truck weights. The probability of simultaneous truck passing is calculated by analyzing video image taken at various locations in highways and national roads. Weigh-In-Motion system data at two locations are used, which is combined with the probability of multiple presence to calculate the multi-lane loading factors for typical 2 lane and 5 lane bridges. Statistical properties of multi-lane loading factors are also calculated assuming that locations for video images and WIM data represent the overall traffic condition in the country. Results are compared with various design codes in the world and they show that the values are between the current Korea Bridge Design Code and AASHTO LRFD specification or Eurocode and are similar to Canadian Code.