• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.135-140
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• 2021

In this paper, we propose an on-line mechanism that simultaneously detects cracks and profiling pavement blocks to detect the displacement of ground surface adjacent to the excavation in the urban area. The proposed method utilizes a 2D laser to profile the information about pavement blocks including the depth and distance among them. In particular, it is designed to enable the detection of cracks and portholes at runtime. For the experiment, real data was collected through Gocator, and trainng was carried out using Faster R-CNN. The performance evaluation shows that our detection precision and recall are more than 90% and the pavement blocks are profiled at the same time. Our proposed mechanism can be used for monitoring management to quantitatively detect the level of excavation risk before a large-scale ground collapse occurs.

• Smart Structures and Systems
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• v.18 no.3
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• pp.405-423
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• 2016

In this study, the feasibility of using telecommunication single-mode optical fiber (SMF) as a distributed fiber optic strain and crack sensor was evaluated in concrete pavement monitoring. Tensile tests on various sensors indicated that the $SMF-28e^+$ fiber revealed linear elastic behavior to rupture at approximately 26 N load and 2.6% strain. Six full-scale concrete panels were prepared and tested under truck and three-point loads to quantify the performance of sensors with pulse pre-pump Brillouin optical time domain analysis (PPP-BOTDA). The sensors were protected by precast mortar from brutal action during concrete casting. Once air-cured for 2 hours after initial setting, half a mortar cylinder of 12 mm in diameter ensured that the protected sensors remained functional during and after concrete casting. The strains measured from PPP-BOTDA with a sensitivity coefficient of $5.43{\times}10^{-5}GHz/{\mu}{\varepsilon}$ were validated locally by commercial fiber Bragg grating (FBG) sensors. Unlike the point FBG sensors, the distributed PPP-BOTDA sensors can be utilized to effectively locate multiple cracks. Depending on their layout, the distributed sensors can provide one- or two-dimensional strain fields in pavement panels. The width of both micro and major cracks can be linearly related to the peak strain directly measured with the distributed fiber optic sensor.

• Journal of the Korean GEO-environmental Society
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• v.10 no.4
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• pp.25-31
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• 2009

In this study, the problem of existing soil pavement is a long-term durability lack and crack occurrence. It complements in order to develop the environmental soil pavement material which composites readily blended mineral binder of liquid and decomposed granite soils. It was estimated optimal mixture proportion for unconfined compressive strength, permeability, $Cr^{6+}$detection test, SEM test with age, freezing and thawing test. It resulted mixture proportion of powder types mineral binder for rates of cement : fly ash : plaster was optimal rates of 50 : 33 : 7, and $Cr^{6+}$detection test as a result was a slight production. SEM test with 3days as a result was made Ettringite. It was found that this material was early development of early-strength for chemical. This study indicated that it will execute field appliciability Evaluation test, examination of soil pavement method with decomposed granite soils and mineral binder.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.3
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• pp.603-610
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• 2017

Various type of pavement deterioration such as crack, bumpy, pothole is rapidly increasing according to the accelerated environmental changes like heavy rainfall, frequent snowing, difference temperature, etc. Accident related to pothole that cause fatal traffic accidents has been increased more than five times over the next five years starting from 2008. As direct or indirect damage by pothole which caused injuries and car damages increases every year, quicker and more efficient management measures are necessary. This study presents the algorithm for materials quantity take-off. The algorithm was suggested by correlation in pothole size and area. Suggested algorithm were confirmed the validity through the 15 field survey in capital area. According to the results of survey, usually the residual materials at which 5~7 kg was generated decreased to 1~2 kg. It showed that automatic pothole detection system is expected not only to reduce materials and resources, but also to contribute to quality improvements of pavement through more accurate material take-off from the situation of constructing rely on their own judgement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.71-82
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• 2021

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.599-608
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• 2017

Cracks on the asphalt road surface can affect the speed of the car, the consumption of fuel, the ride quality of the road, and the durability of the road surface. Such cracks in roads can lead to very dangerous consequences for long periods of time. To prevent such risks, it is necessary to identify cracks and take appropriate action. It takes too much time and money to do it. Also, it is difficult to use expensive laser equipment vehicles for initial cost and equipment operation. In this paper, we propose an effective detection method of road surface defect using ROI (Region of Interest) setting and cany edge detection method using UAV image. The results of this study can be presented as efficient method for road surface flaw detection and maintenance using UAV. In addition, it can be used to detect cracks such as various buildings and civil engineering structures such as buildings, outer walls, large-scale storage tanks other than roads, and cost reduction effect can be expected.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.