• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1111-1119
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• 2022

Tourism projects through islands in the waters of Sinan-gun became active, and as a result, a total of 13 marine bridges connecting islands were completed. However, the marine bridge constructed in the fairway is dangerous for traffic. Particularly, in the case of the marine bridge connecting two islands, the width of the fairway is extremely narrow, therefore the risk is higher. In this study, we evaluated the risk of collision between marine bridge piers and ships using the IALA Waterway Risk Assessment Program (IWRAP), a risk assessment model for port waterways, based on a maritime traffic survey on the coastal bridge in Sinan-gun. The results, indicated that No.1 Sinan bridge had the highest probability of collision and most of the transit ships were coastal passenger ships. In addition, No.1 Sinan bridge was the place where the most collision accidents occurred among the marine bridge piers in the target sea, and the cause this study was analyzed. An analysis of the satellite images of the sea environment of No.1 Sinan bridge using an image processing method, confirmed that obstacles that could not be seen in the chart existed nearby the bridge. As a result, traffic was observed to be concentrated in one direction, unlike two-way traffic, which is a method of inducing traffic of bridges to avoid obstacles. The risk cause analysis method using the image processing technique of this study is expected to be used as a basic research method for analyzing the risk factors of island bridge in the future.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.257-262
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• 2009

Image processing has been utilized for assessment of infrastructure surface coating conditions for years. However, there is no robust method to overcome the non-uniform illumination problem to date. Therefore, this paper aims to deal with non-uniform illumination problems for bridge coating assessment and to achieve automated rust intensity recognition. This paper starts with selection of the best color configuration for non-uniformly illuminated rust image segmentation. The adaptive-network-based fuzzy inference system (ANFIS) is adopted as the framework to develop the new model, the box-and-ellipse-based neuro-fuzzy approach (BENFA). Finally, the performance of BENFA is compared to the Fuzzy C-Means (FCM) method, which is often used in image recognition, to show the advantage and robustness of BENFA.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• Journal of KIBIM
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• v.8 no.1
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• pp.15-23
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• 2018

Geometry control of precast members is the most important technology for modular construction. In this paper, image-based modeling and rendering (IBMR) technology was adopted for 3D modeling of precast elements. It is necessary to use match-casting method for precast post-tensioned column assembly. Preassembly using 3D models created by image processing can minimize construction error. Augmented reality devices are used to check the geometry of the segment. Laboratory-scale tests were performed. The proposed process has been applied to the real precast bridge pier segments.

• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.4
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• pp.48-56
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• 2007

This study was to understand the component that affects the formative beauty and to present the direction of bridge design for improving the image of urban landscape to survey the visual effect and landscape Preference by the change of bridge type. The results of this study are as follows. In the results of image analysis by bridge types, the images of one-arch bridges are unique and interesting, whereas more than two successive arched bridge were harmonize, stable, consecutive and regular. In the case of the arch rib, braced-rib arch bridge was assessed that complicated, diverse and interesting more than solid-rib arch bridge. The results of factor analysis on the psychological factor were classified into three categories: orderliness, aesthetic and symbolism. In the results of analysis on psychological factors by bridge types, the orderliness and symbolism were different in the position of path, and the number of arches, too. In case of arch rib, symbolism was different. In the preference analysis, they showed a sensitive reaction in the background of building. In the results of the relativity preference and psychological factor, according to aesthetic, symbolism and orderliness, there was an effect on the background of building. And, there showed the high effect in order of aesthetic, orderliness and symbolism in the background of mountain and building. This study should be objective raw data of the arch bridge design for improving the urban landscape. In the future, aesthetic variables like colors or textures should be considered for more exact evaluation.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.3
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• pp.75-82
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• 2010

This study formed an estimation of the visual characteristics of urban bridges in Yanghwaro in the Gyeongui Railroad Area using a landscape simulation. Existing theses have formerly only suggested directions for design based on visual preference, but there is as yet no research on the practical process of landscape design. As a result, it is difficult to directly apply this to bridge design. This study found a potential bridge site and presented a direction for bridge design in order to improve the image of the surrounding urban landscape by surveying the visual effects and landscape preferences of different bridge types. An urban landscape was produced using a landscape simulation model and was made the background for the survey. Five bridge types--Girder, Arch, Truss, Cable and Suspension--were selected and presented. The shapes of the bridges were selected based on the floor plan. The results of this study are as follows. In a preference analysis, every bridge except Girder was evaluated as a positive influence. When rating the image, 'artificial' was rated significantly higher than other traits when assessing the background image. When the Girder Bridge was introduced, 'stable' and 'orderly' were both rated highly while 'stable', 'beautiful', 'orderly' and 'interesting' were high with the introduction of the Arch Bridge. 'Beautiful', 'stable', and 'orderly' were given a high value in the introduction of the Truss Bridge and every image except 'natural', 'harmony' and 'orderly' were highly rated in the introduction of the Cable Bridge. Further, every image but 'natural' was highly rated with the introduction of the Suspension Bridge. Based on the analysis of the landscape, there is a difference in preference before and after modeling a bridge type, while the bridge itself is an influence when it is the main object of the simulated scene. This study researched only the shape of the bridge as a part of the landscape but other elements such as stability, economics, and construction are also factors in the design of a bridge. Stability, economics, construction and other factors must be considered when selecting a bridge type in the future.

• Journal of the Korean Society of Propulsion Engineers
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• v.2 no.2
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• pp.1-13
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• 1998

When driven with a short (less than 30$\mu\textrm{s}$) low-energy pulse, the semiconductor bridge(SCB) produces a hot plasma that ignites explosive. The shape of plasma was observed using ultra high speed camera, the generation and the duration time of plasma were estimated by analyzing the ultra high speed camera image. The more energy supplied, the sooner the formation of the plasma was, and the size of the plasma was increased in proportion. The voltage variation of the bridge was measured and analyzed by comparing with the ultra high speed camera image.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.39-39
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• 2011

This study presents an effective vision-based system for cable bridge damage detection. In theory, cable bridges need to be inspected the outer as well as the inner part. Starting from August 2010, a new research project supported by Korea Ministry of Land, Transportation Maritime Affairs(MLTM) was initiated focusing on the damage detection of cable system. In this study, only the surface damage detection algorithm based on a vision-based system will be focused on, an overview of the vision-based cable damage detection is given in Fig. 1. Basically, the algorithm combines the image enhancement technique with principal component analysis(PCA) to detect damage on cable surfaces. In more detail, the input image from a camera is processed with image enhancement technique to improve image quality, and then it is projected into PCA sub-space. Finally, the Mahalanobis square distance is used for pattern recognition. The algorithm was verified through laboratory tests on three types of cable surface. The algorithm gave very good results, and the next step of this study is to implement the algorithm for real cable bridges.