• 한국ITS학회 논문지
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• 제18권5호
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• pp.183-193
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• 2019

본 연구에서는 전/후방 차량 검지가 가능한 차량센서를 탑재한 프로브 차량으로 수집한 주행 궤적을 이용하여 연속웨이블릿변환 기반 충격파 검지 및 소멸 시점 예측 방법을 제안하였다. 제안된 방법의 효과성 분석을 위하여 충격파 소멸점 간 거리오차와 충격파 소멸점 시간-위치 오차를 평가 지표로 제시하였고, 교통혼잡 수준, 속도 감소 현상 지속시간, 프로브 차량의 비율 등을 실험요인으로 하는 미시적 시뮬레이션 기반의 실험을 통하여 제안된 방법의 개념을 검증하였다. 그 결과, 두 가지 평가 지표 모두 교통혼잡 수준 및 속도 감소 지속시간에 크게 민감하지 않음을 보임으로서, 본 연구에서 제안하는 방법이 임의의 위치와 시간 동안 발생하는 속도 감소 현상으로 인한 충격파를 검지하고 그 소멸시점을 예측하는데 효과가 있음을 보였다. 그리고 그 정확도는 전체 차량 중 프로브 차량의 비율에 많은 영향을 받는 것으로 나타났다.

• Ecology and Resilient Infrastructure
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• 제8권3호
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• pp.143-153
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• 2021

초음파의 고주파 (1.6 MHz), 저주파 (23 kHz) 조사를 통해 Microcystis aeruginosa (M. aeruginosa)의 성장 억제 효과 (growth inhibition effect)를 대용량 (7.2 L) 조류 시료를 활용해 실험실 규모 (lab-scale) 실험을 통해 검증하였다. 6시간 고주파 조사 후 chl-a 농도와 M. aeruginosa 개체수는 점진적인 감소 경향이 관측된 반면, 6시간 저주파 조사 후 chl-a 농도 및 M. aeruginosa 개체수가 고주파 대비 급격하게 감소하는 것을 확인하였다. 또한, 초음파 조사기간 보다 조사종료 이후의 일차분해상수(k)가 큰 것으로 확인되었으며, 이러한 결과는 기존 연구에 비해 비교적 낮은 단위부피당 초음파 에너지가 조류 세포막과 내부 기관에 미치는 영향이 지연되어 일정 시간 이후 점진적인 조류 성장 억제 효과가 관측되는 것으로 사료된다. 초음파 조사 후 chl-a 농도와 M. aeruginosa 개체수 변화, 성장률과 일차분해율을 종합적으로 비교한 결과, 저주파에서 조류 성장 억제 효과가 우수한 것으로 확인되었으며, 저주파에서 M. aeruginosa 세포막으로 에너지 투과 효율이 우수해 기낭 등 내부기관에 더 큰 손상을 유도하기 때문인 것으로 사료된다. SEM과 TEM image 관측을 통해서도 고주파 보다 저주파에서의 M. aeruginosa의 세포 표면 및 세포막의 손상이 명확하게 관측되었다. 마지막으로 초음파에 의한 M. aeruginosa의 기낭 파괴 및 세포막의 기능 손상을 통해 용출되는 독성물질인 microcystin-LR의 수중 유출은 검출한계 (0.1 ㎍ L^-1) 미만으로 용출되어 수생태계에 미치는 유해성은 미미한 것으로 판단된다.

• Smart Structures and Systems
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• 제30권6호
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• 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.

• Geomechanics and Engineering
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• 제35권6호
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• pp.603-615
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• 2023

Population growth and urbanization prompted engineers to propose more sophisticated and efficient transportation methods, such as underground transit systems. However, due to limited urban space, it is necessary to construct these tunnels in close proximity to existing infrastructure like high-rise buildings and bridges. Battered piles have been widely used for their higher stiffness and bearing capacity compared to vertical piles, making them effective in resisting lateral loads from winds, soil pressures, and impacts. Considerable prior research has been concerned with understanding the vertical pile response to tunnel excavation. However, the three-dimensional effects of tunnelling on adjacent battered piled foundations are still not investigated. This study investigates the response of a single battered pile to tunnelling at three critical depths along the pile: near the pile shaft (S), next to the pile (T), and below the pile toe (B). An advanced hypoplastic model capable of capturing small strain stiffness is used to simulate clay behaviour. The computed results reveal that settlement and load transfer mechanisms along the battered pile, resulting from tunnelling, depend significantly on the tunnel's location relative the length of the pile. The largest settlement of the battered pile occurs in the case of T. Conversely, the greatest pile head deflection is caused by tunnelling near the pile shaft. The battered pile experiences "dragload" due to negative skin friction mobilization resulting from tunnel excavation in the case of S. The battered pile is susceptible to induced bending moments when tunnelling occurs near the pile shaft S whereas the magnitude of induced bending moment is minimal in the case of B.

• Wind and Structures
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• 제6권2호
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• pp.141-150
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• 2003

This paper presents galloping analysis of multiple-degree-of-freedom (MDOF) structural roofs with multiple orientations. Instead of using drag and lift coefficients and/or their combined coefficient in traditional galloping analysis for slender structures, this study uses wind pressure coefficients for wind force representation on each and every different orientation roof, facilitating the galloping analysis of multiple-orientation roof structures. In the study, influences of nonlinear aerodynamic forces are considered. An energy-based equivalent technique, together with the modal analysis, is used to solve the nonlinear MDOF vibration equations. The critical wind speed for galloping of roof structures is derived, which is then applied to galloping analysis of roofs of a stadium and a high-rise building in China. With the aid of various experimental results obtained in pertinent research, this study also shows that consideration of nonlinear aerodynamic forces in galloping analysis generally increases the critical wind speed, thus enhancing aerodynamic stability of structures.

• 한국항공운항학회:학술대회논문집
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• 한국항공운항학회 2016년도 춘계학술대회
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• pp.151-161
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• 2016

As can be seen by the fact that 96% of foreign visitors to Korea travel by air, governments across Asia are aware of the importance civil aviation has in a country's economical developlment, and are fiercely investing in infrastructure and systems which could help set the ground for a hub strategy, catching the profits generated from air travel. In the case of Incheon Internation Airport (IIA), Chinese passengers have outnumbered Japanese passengers using IIA. Considering the potential growth of the Chinese air transport market, there is the growing need for thorough research regarding Chinese passengers. Surveys for the following study were distributed between September 2015 and October 2015, which includes Chinese holidays. Surveys were distributed to Chinese transfer passengers, with questions related to airport service and facility usage.

• 항공우주시스템공학회지
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• 제9권1호
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• pp.19-27
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• 2015

The Technical Standard Order articles are the parts and appliances for which the civil aviation authority designates as it is necessary to standardize for the expedited certification process and aviation safety. TSO articles were used on the type certified aircraft as replacement parts in the early days of TSO authorization system, but those articles are widely used on the newly developed aircraft as well in these days. In this paper, we compared the differences of the TSO authorization systems between FAA and EASA, and proposed the rulemaking items to improve Korean TSO authorization system and to contribute to growth of aviation industry.

• Smart Structures and Systems
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• 제14권2호
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.