• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.16 no.1
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• pp.37-44
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• 2012

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.20 no.1
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• pp.38-41
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• 2016

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.1
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• pp.199-206
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• 2003

This paper presents the fatigue life of transverse joint of concrete pavement with the fatigue model test. A 1/12 scale model was used to satisfy the geometric load, material similitude, which are variables to the skew angel of transverse joint. From the test results by fatigue load 700kgf applied, we can have that the fatigue life of transverse joint with skew angle is better than that of transverse joint without skew angle. In addition, we can have that the fatigue life of skewed transverse joint with angle of 10 degree is better than that of skewed transverse joint with angle of 20 degree.

• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.3
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• pp.203-211
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• 2001

This paper presents load transfer efficiency of skewed transverse joint of jointed concrete pavement with the fatigue model test. A 1/12 scale model was used to satisfy the geometry, loading, material similitude, which are variables to the skew angel of transverse joint. As the test results by fatigue load 700kgf applied, the deflection and stress of transverse joint were decreased as to increasing of skew angle of transverse joint. In addition, load transfer efficiency of transverse joint with skew angle is better than the load transfer efficiency of transverse joint without skew angle.

• Smart Structures and Systems
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• v.32 no.5
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.453-454
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• 2010

Statical analysis for determination of LOT size was performed to make inspection standards on concrete pavement site applied correspondingly to KS Q 0001. As a result, minimum sampling frequency of strength inspection to certify the required flexural strength was analysed once for each $300m^3$ or once per day, nor less than.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.587-598
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• 2012

Successful implementation of infrastructure asset management system can be started with rich and reliable data. However, measurement errors in the data have always existed in the real world caused for many unknown reasons. It disturbs maintenance activities of agencies, and makes negative effects to reliability of research results on forecasting deterioration process and life cycle cost. Above all, it makes a contradiction that road agencies cannot believe their inspection data surveyed by their hands. It is particularly serious in the road pavement management field. Although road agencies are well recognized the fact, inspecting without measurement error would be a great challenge. Considering the facts, this paper aimed to suggest statistical error processing methods to correct latent error included in pavement surface inspection data. As alternatives, this paper suggested two methods based on probability distribution to consider structure of error and reliability of the data. The suggested methods were empirically tested by using pavement inspection data from Korean National Highway. As the result, this paper confirmed that conventional error processing that just removes only visible errors is not enough to cover uncertainty in pavement deterioration process. The suggested methods would be useful for improving reliability of analysis results required for road infrastructure asset management.

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.19 no.3
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• pp.43-49
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• 2015

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.532-541
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• 2016

The objective of this paper is to support accurate pavement condition assessment and decision of proper maintenance method and time by conducting visual inspection and calculating the remaining life of pavement from falling weight deflectometer(FWD) data. Each was implemented in the same long-term performance pavement(LTPP) sections. Visual inspection was executed to measure pavement condition indices such as crack, rutting and international roughness index(IRI) and the Seoul Pavement Index(SPI) was calculated based on these results. The dynamic modulus was back-calculated from the FWD data. The remaining pavement lives were determined from equivalent single axle loading(ESAL) and FWD data. Correlation of maintenance priority by each result value was examined. Consequently, the correlation between remaining life to Crack and Rutting was higher than the other factors or indicesbecause IRI is not related to FWD value and SPI value consists with IRI value and other indices. The R-square value of correlation of FWD with Crack and Rutting was 0.65, which indicated an insufficient correlation. Consequently, when decision of maintenance of method, time, etc. is determined, FWD data have to be considered with Crack and Rutting because of those relations.

• 한국도로학회:학술대회논문집
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• 2004.10a
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• pp.17-20
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• 2004