• 수산해양기술연구
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• 제57권3호
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• pp.228-235
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• 2021

Marine accidents caused by ships are very diverse, such as collision, sinking, stranding, grounding and fire. In particular, persons on passenger ship are unspecified and not trained, so it makes evacuation harder. For this reason, an evacuation plan that considers diverse situation in ship is needed. Effective evacuation planning requires training in consideration of various evacuation situations. In this paper, we investigated the time elapsed on evacuation in various situations from "HANMIR," the fishery training ship of the Korean Institute of Maritime and Fisheries Technology, using a Ship Evacuation Analysis Program (SEA-Pro) which is introduced to the society. We assumed a situation that has not only inconveniences for real training but also the possibilities of happening. Not all trainees are resting in their cabin, so we assumed positions such as they are in the bridge or engine room and applied fire and flooding situations. We assumed that the time for alerting the situation would be short, so we applied only elapsed time of movement. Those analyses could be helpful in three ways. The first is predicting the consequence of possible accidents. There are some conditions that can be appliable to this model, such as the decreased area of sight in those situations. The second is concluding the optimal limit of carriage and placement of safety instruments on building a new ship. The third is to be a base data for ships to make a new effective evacuation plan based on these analyses.

• Ecology and Resilient Infrastructure
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• 제7권4호
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• pp.353-365
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• 2020

본 연구에서는 낙동강을 대상으로 HEC-RAS를 이용하여 보 개방에 따른 하상변동 특성을 분석하였다. 연구 대상 지역은 낙동강 구담교 하류부터 하구둑까지 292.37 km 구간이다. 하상변동 모의를 위하여 1차원 수치해석모델인 HEC-RAS 프로그램을 사용하였으며 2017년도부터 2019년도까지 2가지의 시나리오로 모의를 수행하였다. 시나리오 1은 수문 전면 개방의 조건으로 구축하였으며 시나리오 2는 수문이 설치된 조건으로 모의를 수행하였다. 모의결과, 시나리오 1의 조건에서 합천창녕보 - 창녕함안보 대부분의 구간 (약 40 km)에서 퇴적이 발생한 것을 확인하였다. 또한 낙동강 본류 내의 유사를 포함한 흐름이 보 구조물로 인해 단절되지 않고 연속적으로 하상을 변동시키는 것으로 예측되었다.

• Computers and Concrete
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• 제30권3호
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• pp.175-183
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• 2022

To study the working mechanism and size effect of an innovative dovetail UHPC joint originated from the 5th Nanjing Yangtze River Bridge, a large-scale testing subject to negative bending moment was conducted and compared with the previous scaled specimens. The static responses, i.e., the crack pattern, failure mode, ductility and stiffness degradation were analyzed. It was found that the scaled specimens presented similar working stages and working mechanism with the large-scale ones. However, the post-cracking ductility and relative stiffness degradation all decrease with the enlarged length/scale, apart from the relative stiffness after flexural cracking. The slab stiffness at the flexural cracking stage is 90% of the initial stiffness while only 24% of the initial stiffness reserved in the ultimate stage. Finite element model (FEM) was established and compared with the experiments to verify its effectiveness in exploring the working mechanism of the innovative joint. Based on this effective method, a series of FEMs were established to further study the influence of material strength, pre-stressing level and ratio of reinforcement on its deflection-load relationship. It is found that the ratio of reinforcement can significantly improve its load-carrying capacity among the three major-influenced factors.

• Geomechanics and Engineering
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• 제27권6호
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• pp.561-571
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• 2021

Cofferdams made of teel sheet piles are commonly utilized as support structures for excavation of sea-crossing bridge foundations. As cofferdams are often subject to tide variation, it is imperative to consider potential effects of tide on stability and serviceability of sheet piles, particularly, ultralong steel sheet piles (USSPs). In this study, a real USSP cofferdam constructed using new construction technology in Nanxi River was reported. The design of key parts of USSP cofferdam in the presence of tidal action was first introduced followed by the description of entire construction technology and associated monitoring results. Subsequently, a three-dimensional finite-element model corresponding to all construction steps was established to back-analyze measured deflection of USSPs. Finally, a series of parametric studies was carried out to investigate effects of tide level, soil parameters, support stiffness and construction sequence on lateral deflection of USSPs. Monitoring results indicate that the maximum deflection during construction occurred near the riverbed. In addition, measured stress of USSPs showed that stability of USSP cofferdam strengthened as construction stages proceeded. Moreover, the numerical back-analysis demonstrated that the USSP cofferdam fulfilled the safety requirements for construction under tidal action. The maximum deflection of USSPs subject to high tide was only 13.57 mm at a depth of -4 m. Sensitivity analyses results showed that the design of USSP cofferdam system must be further improved for construction in cohesionless soils. Furthermore, the 5th strut level before concreting played an indispensable role in controlling lateral deflection of USSPs. It was also observed that pumping out water before concreting base slab could greatly simplify and benefit construction program. On the other hand, the simplification in construction procedures could induce seepage inside the cofferdam, which additionally increased the deflection of USSPs by 10 mm on average.

• Smart Structures and Systems
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• 제29권1호
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• 한국구조물진단유지관리공학회 논문집
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• 제26권3호
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• pp.21-28
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• 2022

TMD는 다른 진동제어장치에 비해 구조가 단순하며, 구조물에 발생하는 정형화된 형태의 진동에 우수한 제어성능을 보인다. 그러나 다른 제어장치에 비하여 진동제어범위가 좁아 예상치 못한 외부하중으로 인하여 발생하는 진동주기에는 취약하다. 본 연구에서 개발된 ETMD는 Mass를 전자석으로 구성하여 전류를 공급할 때 자기장이 형성됨과 동시에 마찰판과의 마찰력을 상승시켜 Mass의 거동을 순간적으로 제어한다. 개발된 ETMD의 제어성능 평가를 위하여 모형 단순보 교량 중앙에 ETMD를 설치한 후 중앙부 최대 수직변위가 발생하는 3.02Hz 조건으로 강제 진동을 발생시켜 휨거동 제어실험을 수행하였다. 실험결과 ETMD는 중앙부 최대 수직변위가 발생하는 3.02Hz에서 변위 감쇠율이 57.51%로 우수한 제어성능을 발휘하며 그 외에 진동주기에도 안정적인 제어성능을 보이는 것을 확인하였다.

• 한국시뮬레이션학회논문지
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• 제19권3호
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• pp.127-135
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• 2010

최근의 현대화된 다양한 항해장비들로 인해 선박에 있는 항해사들은 위험상황 인식에 도움이 될 수 있는 정보들을 획득할 수 있게 되었다. 하지만, 이러한 유용한 도구들에도 불구하고 항해사들은 여전히 안전항행을 위한 의사결정에 어려움을 겪고 있는데, 이는 다양한 장비들이 제공하는 선박 내 외 상황에 관한 많은 양의 데이터를 지속적으로 관찰해야 한다는 항해사의 부담과 여러 장비 간 정보의 불일치성 때문이다. 실제로, 많은 해양 사고가 항해사의 부주의에 의해 일어나고 있다는 것이 이미 잘 알려져 있다. 따라서, 항행 상황의 일부 정보만을 제공하는 보조 장비를 넘어서 항해사의 의사결정을 도울 수 있는 지원도구가 요구되고 있다. 시뮬레이션은 의사결정을 지원 할 수 있는 기술 중 하나며, 선박에서의 실시간 주변상황에 대한 종합적인 평가 및 예측 가능한 시스템은 항해사의 안전항행에 대한 의사결정에 도움을 줄 수 있다. 이 논문은 선박을 위한 항행안전정보 시스템에서의 위험 상황 예측을 위한 시뮬레이션 방안에 관한 연구로서, 다양한 지식 베이스 및 이산 사건 시뮬레이션 방식을 활용한 시스템 전체 구성 방법을 제안하고 제한된 항행상황 시나리오에서의 구성 요소들의 예시를 통해 시스템의 타당성을 보인다.

• 융합경영연구
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• 제5권2호
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• pp.10-32
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• 2017

The landscape of corporate reporting is changing quickly. The concepts, elements and principles that characterize the way organizations plan, manage and report their annual performances are currently being questioned, debated, and redesigned throughout the world. However, widening the scope of corporate performance and reporting is a major issue. Research needs to bridge the gap between social and financial performance by considering corporate performance in a wider perspective. At base, IR is a relatively new but powerful idea: enhancing the way organizations think, plan and report the story of their business. Organizations are using IR to communicate a clear, concise, integrated story that explains how all of their resources are creating value. This paper examines the rise of what has been widely claimed to represent a new and striking future for corporate reporting, namely the notion of "Integrated Reporting" (IR). Unfortunately, there is poor empirical research work undertaken which has focused on published integrated reports. This research study provides initial analysis of the content and structure of the corporate integrated reports published in 2013 and available on the International Integrated Reporting Council (IIRC) Emerging Examples Database. As part of this study, Integrated Reports were analyzed for company information, report information and report content. Moreover, they were also evaluated as to the extent these adhered to the integrated reporting (IR) Guiding Principles, Content Elements, and the multiple capitals model. Findings of this study indicate that "early integrated reports were mostly lengthy, fail to adhere to all the guiding principles, and covered four of the six capitals suggested." At present, no universally accepted global framework for IR exists and it is still largely a voluntary practice. We believe that IR of both financial and non-financial performance should be made mandatory, and it should become a universal practice for all the global listed companies within the next 5-10 years.

• Smart Structures and Systems
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• 제32권1호
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• Smart Structures and Systems
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• 제31권3호
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.