• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.2
• /
• pp.67-74
• /
• 2019

In this paper the response factor is investigated for middle and small size-RC slab aged bridges. The response factor consists of static and dynamic response factors and is a main parameter in the frequency based-bridge load carrying capacity prediction model. Static and dynamic response factors are determined based on the frequency variation and the impact factor variation respectively between current and previous (or design) states of bridges. Here, the impact factor variation is figured out using the impact factor response spectrum which provides the impact factor according to the natural frequency of bridges. In this study, four actual RC slab bridges aged over 30 years after construction are considered and their span length is 12m. The dynamic loading test in field using a dump truck and eigenvalue analysis with FE models are conducted to identify the current and previous (or design) state-natural frequencies of the bridges, respectively. For more realistic considerations in the moving loading situation, the impact factor response spectrum is developed based on tri-axle moving loads representing the dump truck load distribution and various supporting conditions such as simply supported and both ends fixed conditions. From the results, the response factor is widely ranged from 0.21to 0.91, showing that the static response factor contributes significantly on the results while the dynamic response factor has a small effect on the result. Compared to the results obtained from the impact factor response spectrum based on the single axle-simply supported condition, the maximum percentage difference of the response factors is below 3.2% only.

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

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.3D
• /
• pp.453-459
• /
• 2006

This paper presents an experimental evaluation of wandering effect on asphalt concrete pavement responses. A laser-based wandering system has been developed and its performance is verified under various field conditions. The portable wandering system composed of two laser sensors with Position Sensitive Devices can allow one to measure the distance between laser sensors and tire edges of moving vehicle. Therefore, lateral position of each wheel on the pavement can be determined in a real time manner. Pavement responses due to different loading paths are investigated using a roll over test which is carried out on one of asphalt surfaced pavements in the Korea Highway Corporation test road. The pavement section (A5) consists of 5 cm thick surface course; 7 cm intermediate course; and 18 mm base course, and is heavily instrumented with strain gauges, vertical soil pressure cells and thermo-couples. From the center of wheel paths, seven equally-spaced lateral loading paths are carefully selected over an 140 cm wandering zone. Test results show that lateral horizontal strains in both surface and intermediate courses are mostly compressive right under the loading path and tensile strains start to develop as the loading offset becomes 40 cm from the wheel path. The development of the vertical stresses in the top layers of subbase and anti-frost is found to be minimal once the loading offset becomes 50 cm.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.916-923
• /
• 2023

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site 'A', objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site 'B' showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.