• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2011년도 추계학술대회
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• pp.13-15
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• 2011

This report represents analysis of the maritime casualty in terms of Bridge Resource Management. We evaluate the attitudes and knowledge of bridge officer regarding human factors issues that have been identified as causal to mishaps in high-risk situations. So to reduce human errors our goal is to establish effective officer resource management (ORM) program which is based on all subjects for cadets in IMO model course. In harmonization with STCW(The International Convention on Standards of training, Certification and Watch-keeping for Seafarers), as the result, the curriculumss in the maritime education institutions is surveyed to improve our education system and then reduce the human errors by mariners at sea.

• Smart Structures and Systems
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• 제30권6호
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• 한국지진공학회논문집
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• 제10권6호
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• pp.1-8
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• 2006

본 논문에서는 교량 모니터링 시스템의 일부분으로 서해대교에 설치된 교량 하중측정 시스템(BWIM system)으로부터 획득한 신호를 분석하여 통행차량의 정보를 추출하기 위한 알고리즘의 개발과정과 이를 위해 수행한 현장 차량주행시험에 대하여 기술하였다. 개발된 BWIM 시스템은 포장층에 매설하는 축감지기가 없는 형태로, 바닥판과 가로보에 설치된 변형률계로부터 측정한 시간이력 변형률신호만을 이용하였다. 이들 측정신호로부터 추출하고자 하는 차량의 정보는 통과차로, 통과속도, 차 축수 및 총 중량이며, 이들 정보의 추출을 위해 패턴인식기법의 일종인 인공신경망(Aritificial Neural Network, ANN) 기법을 사용하였다. 현장 차량주행시험을 통하여 기지차량 및 미지차량 통행시의 BWIM 응답 데이터를 측정하였으며, 이들 실측데이터를 사용하여 인공신경망의 학습 및 성능검증을 수행하였다. 개발된 기법을 사용하여 추출되는 차량의 정보들은 현재의 교량상태 및 피로수명 평가시 활용될 수 있을 것이며, 향후 설계트럭 하중모델의 개정시 기초자료로도 활용될 수 있을 것으로 기대된다.

• 해양환경안전학회지
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• 제24권6호
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• pp.701-708
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• 2018

인적오류는 해양사고의 중요한 원인이고, 인적오류의 식별은 해양사고 예방에 근간이 된다. 특히, 선교팀(항해사와 조타수)이 주어진 상황에서 취한 기술적인 행동 패턴은 인적오류 식별에 중요한 정보를 제공한다. 본 연구의 목적은 익수자 구조를 위한 윌리암슨 선회법(Williamson's Turn)을 이용하여 선교팀들의 기술적인 행동 패턴을 식별하고 분석하기 위한 것이다. 이를 위한 본 연구의 핵심은 실험을 실시하는 과정에서 나타난 선교팀의 인적 행동 요인에 대한 인지모델을 구축하고 분석하는 것이다. 실험환경은 선박조종 시뮬레이터를 이용하여 구축하고, 24개 선교팀으로 구성된 참가자들을 대상으로 실험을 진행하였다. 실험결과, 방향타와 기관을 사용한 항적유지와 선박조종에 대한 행동 패턴을 식별할 수 있었다. 본 연구는 선원의 자격 및 훈련에 적용하여 선교팀의 인적오류를 보완하고 보정하는데 기여할 수 있을 것으로 기대된다.

• Structural Monitoring and Maintenance
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• 제1권3호
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.202-210
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• 2005

This paper presents a new neural network control for nonlinear bridge systems with earthquake excitation. We design multi-layer neural network controllers with a single hidden layer. The selection of an optimal number of neurons in the hidden layer is an important design step for control performance. To select an optimal number of hidden neurons, we progressively add one hidden neuron and observe the change in a performance measure given by the weighted sum of the system error and the control force. The number of hidden neurons which minimizes the performance measure is selected for implementation. A neural network was trained for mitigating vibrations of bridge systems caused by El Centro earthquake. We applied the proposed control approach to a single-degree-of-freedom (SDOF) and a two-degree-of-freedom (TDOF) bridge system. We assessed the robustness of the control system using randomly generated earthquake excitations which were not used in training the neural network. Our results show that the neural network controller drastically mitigates the effect of the disturbance.

• Smart Structures and Systems
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• 제13권6호
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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.

• 한국안전학회지
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• 제34권5호
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• pp.103-110
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• 2019

In order to reduce the accidents occurring at construction sites, it is necessary to approach with harmonious measures considering various aspects such as systems, training, facilities, and protection equipments. Suggestion of safe construction method can be a good alternative. In the previous study, the half-depth precast deck with RC rib panel was proposed as an alternative method for safe bridge deck construction, and the performance required by the design code was verified through a four-point bending test. But the actual bridge deck is subjected to the repetitive action of the wheel load rather than the bending condition due to the four-point load. In this study, fatigue test was performed by repeating the wheel load $2{\times}10^6$ cycles to verify the safety of the half-depth precast deck with RC rib panel under actual conditions. As a result, fatigue effect due to repetition of wheel load was not significant in terms of serviceability such as crack width and deflection. In the residual strength test conducted after the fatigue test, the half-depth precast deck with RC rib panel failed by punching shear which is typical failure mode of bridge decks and the residual strength was similar to the punching strength of the RC and PSC bridge decks in spite of the fatigue effects.

• Computers and Concrete
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• 제11권5호
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• pp.383-397
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• 2013

Two-dimensional tensile stresses are occurring at the back of the anchorage of the tendons of prestressed concrete bridges. A new method named "tensile stress region" for the design of the reinforcement is presented in this paper. The basic idea of this approach is the division of an anchor block into several slices, which are described by the tensile stress region. The orthogonal reinforcing wire mesh can be designed in each slice to resist the tensile stresses. Additionally the sum of the depth of every slice defined by the tensile stress region is used to control the required length of the longitudinal reinforcement bars. An example for the reinforcement design of an anchorage block of an external prestressed concrete bridge is analyzed by means of the new presented method and a finite element model is established to compare the results. Furthermore the influence of the transverse and vertical prestressing on the ordinary reinforcement design is taken into account. The results show that the amount of reinforcement bars at the anchorage block is influenced by the layout of the transverse and the vertical prestressing tendons. Using the "tensile stress region" method, the ordinary reinforcement bars can be designed more precisely compared to the design codes, and arranged according to the stress state in every slice.

• 대한물리의학회지
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• 제17권3호
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• pp.1-10
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• 2022

PURPOSE: This study examined the effects of the resistance levels on the muscle activities around the hip and spine during bridge exercise with hip abduction resistance in patients with chronic back pain. METHODS: A cross-over study design was used. Twenty subjects with low back pain were enrolled in this study. The subjects performed bridge exercises with hip abduction resistances (20 mmHg, 40 mmHg, and 60 mmHg). A Narrow Sling was used to provide resistance. Surface electromyography was used to measure the activity of the erector spinae, biceps femoris, gluteus maximus, and gluteus medius. RESULTS: The muscle activity of the gluteus maximus and gluteus medius increased significantly with increasing resistance levels. There was a significant difference in the muscle activity of the biceps femoris with a resistance level between 20 mmHg and 40 mmHg, but there was no significant difference in the other resistance levels. There was no significant difference according to resistance level in the erector spinae. The muscle activity ratios of the gluteus medius/erector spinae and gluteus maximus/erector spinae increased significantly with increasing resistance strength. CONCLUSION: The different levels of abduction resistance for hip abduction during bridge exercise will help activate the gluteus maximus selectively in patients with chronic back pain.