• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.5
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• pp.757-767
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• 2016

The power spectral density (PSD) for the road surface roughness on the bridges of minor roads in Wonju city and Hoengseong-gun, Gangwon-do is presented. To obtain the PSD, the road surface roughness on 18 different bridges with various superstructure type and span is measured by GPS at every 10 to 30cm interval. Assuming the PSD as the stationary normal probability process with zero mean value, the PSD of measured road surface roughness is obtained by applying the Maximum Entropy Method (MEM). A simple formula in evaluating the PSD of RC slab bridge, Rahmen bridge and PSC I-girder bridge which is applicable to the dynamic response analysis of bridges considering the road surface roughness is proposed. Using the calculated PSD curves, the road surface conditions on the 18 bridges are evaluated. The statistical relationship between the PSD and the IRI is presented by applying linear regression and correlation analysis.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.223-232
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• 2021

In general, stormwater flows to the road surface, especially in urban areas, and it is discharged through the drainage grate inlets on roads. The appropriate evaluation of the road drainage capacity is essential not only in the design of roads and inlets but also in the design of sewer systems. However, the method of road surface flow analysis that reflects the topographical and hydraulic conditions might not be fully developed. Therefore, the enhanced method of road surface flow analysis should be presented by investigating the existing analysis method such as the flow analysis module (uniform; varied) and the flow travel time (critical; fixed). In this study, the algorithm based on varied and uniform flow analysis was developed to analyze the flow pattern of road surface. The numerical analysis applied the uniform and varied flow analysis module and travel time as parameters were conducted to estimate the characteristics of rainfall-runoff in various road conditions using the developed algorithm. The width of the road (two-lane (6 m)) and the slope of the road (longitudinal slope of road 1 - 10%, transverse slope of road 2%, and transverse slope of gutter 2 - 10%) was considered. In addition, the flow of the road surface is collected from the gutter along the road slope and drained through the gutter in the downstream part, and the width of the gutter was selected to be 0.5 m. The simulation results were revealed that the runoff characteristics were affected by the road slope conditions, and it was found that the varied flow analysis module adequately reflected the gutter flow which is changed along the downstream caused by collecting of road surface flow at the gutter. The varied flow analysis module simulated 11.80% longer flow travel time on average (max. 23.66%) and 4.73% larger total road surface discharge on average (max. 9.50%) than the uniform flow analysis module. In order to accurately estimate the amount of runoff from the road, it was appropriate to perform flow analysis by applying the critical duration and the varied flow analysis module. The developed algorithm was expected to be able to be used in the design of road drainage because it was accurately simulated the runoff characteristics on the road surface.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of Korean Society of Forest Science
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• v.97 no.6
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• pp.656-660
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• 2008

This study is carried out to minimize the damage to the forest road when locating strip roads in the future for stability of timberland after afforestation by assessing the factors that affect the damage on the forest road surface and making appropriate constructing standards. Major factors that influence damage to the strip road surface were location, longitudinal gradients, soil types, cross-section shape in order of influence on damage. it is considered that structural road factors like longitudinal gradients, road width, location factors such as construction location, slope gradients and road material like soil types were greatly related to occurrence of road surface damage. Damage occurrences in the forest road were severe at the valley, longitudinal gradients of over 24%, weathered granite soil, concave of road position, road width of over 3.0 m. stability was high at longitudinal gradients of 4~24%, road width of under 3.0 m, ridge of road position, straight slope, soil materials. The evaluation table of damage possibility on forest road was manufactured by discriminant analysis using Quantification theory(II). The results showed that the discriminant ratios was 79.4% and this table was available for forest manager.

• Structural Engineering and Mechanics
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• v.11 no.3
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• pp.259-272
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• 2001

The impact behavior of a multigirder concrete bridge under single and multiple moving vehicles is studied based on correlated road surface characteristics. The bridge structure is modeled as grillage beam system. A 3D nonlinear vehicle model with eleven degrees of freedom is utilized according to the HS20-44 truck design loading in the American Association of State Highway and Transportation Officials (AASHTO) specifications. A triangle correlation model is introduced to generate four classes of longitudinal road surface roughness as multi-correlated random processes along deck transverse direction. On the basis of a correlation length of approximately half the bridge width, the upper limits of impact factors obtained under confidence level of 95 percent and side-by-side three-truck loading provide probability-based evidence for the evaluation of AASHTO specifications. The analytical results indicate that a better transverse correlation among road surface roughness generally leads to slightly higher impact factors. Suggestions are made for the routine maintenance of this type of highway bridges.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.165-173
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• 2008

This study aims at generating automatically three dimensional geometric models of roads using LIDAR data and a digital map. The main processes in the proposed method are (1) generating a polygon encompassing a road region using a road layer from the digital map, (2) extracting LIDAR points within the road region using the polygon, (3) organizing the points into surface patches and grouping the patches into surface clusters, (4) searching the road surface clusters and generating the surface model from the points linked to the clusters, (5) refining the boundary using a digital map. By applying the proposed method to real data, we successfully generated the linear and surface information of the roads.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.2
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• pp.34-43
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• 2007

To investigate the restoration procedure on soil physical properties and vegetation at the surface of strip road affected by timber harvesting operation. This study was carried out at strip roads constructed between 1989 and 1994 in Mt. Baekun, Kwangyang, Chollanam-Do. Soil hardness of the surface layer was improved with change of time after strip road construction, but that of 7.6~15 cm depth from the surface was not improved. According to linear regression analysis, it was estimated 16.6 years in 0~7.5 cm and 16.9 years in 7.6~15 cm soil depth to be restored to natural forest lands. The amount of surface soil erosion was 0.045$m^3$/km/yr on strip roads constructed in 1989 and 1990, and road constructed in 1994 showed the highest value (4.5$m^3$/km/yr). Vegetation coverage rates of road surface were 96.7% in strip roads constructed in 1990. Those of cutslope and fillslope were highest in roads constructed in 1990. The results indicated that strip roads were restored with change of time after road construction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.917-922
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• 2009

This paper presents a technique to generate road surface profiles in a spatial domain using a power spectral density function. A single track power spectral density function is proposed to describe a road surface profile, which is also applicable for multi-track vehicle response analysis. The roads in lateral direction makes the relation between the coherence of the lateral tracks. The derived road surfaces are compared to ISO(International Organization for Standardization) standards. Generated road profiles are in good agreements with the target road PSD shape and measured coherence relation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.213-220
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• 2016

In order to drive on road safely, the type of road design and construction is basically needed to optimize driver's safety and vehicle performance. Although the heavy traffic highways were built by reflecting these factors, the national highways and local roads have still taken a lot of problems. In this study, we analyzed the behavior characteristics of a vehicle according to the speed variation of the vehicle using the PC-Crash program for the traffic accidents reconfiguration at GULUN interchange located Hongcheon in Gangwon Province. the conditions outlet surface of the road for analysis were dry road surface, wet road surface and icy road surface. As a result, we identified the fact that the friction coefficient of road surface and the speed of vehicle affected to vehicle behavior characteristics of outlet shape in GULUN interchange, and showed the possibility that we can verify a problem about road design through to this simulation in advance.