• Smart Structures and Systems
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• 제31권1호
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• pp.13-27
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• 2023

Fatigue crack is a fatal problem for steel structures. Early detection and maintenance can help extend the service life and prevent hazards. This paper presents the ultrasonic guided waves-based (UGWs-based) fatigue crack detection of a steel I-beam. The semi-analytical finite element model has been built to obtain the wave propagation characteristics. Damage indices in both time and frequency domains were analyzed by considering the characteristic variations of UGWs including the amplitude, phase angle, and wave packet energy. The pulse-echo and pitch-catch methods were combined in the detection scheme. Lab-scale experiments were conducted on welded steel I-beams to verify the proposed method. Results show that the damage indices based on the characteristic variations in the time domain can identify and localize the fatigue crack before it enters the rapid growth stage. The damage severity can be reasonably evaluated by analyzing the time-domain damage indices. Two nonlinear damage indices in the frequency domain give earlier warnings of the fatigue crack than the time-domain damage indices do. The identification results based on the above two nonlinear indices are found to be less consistent under various excitation frequencies. More robust nonlinear techniques needed to be searched and tested for early crack detection in steel I-beams in further study.

• 한국산업융합학회 논문집
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• 제26권5호
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• pp.953-965
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• 2023

The stratospheric drones are developed to perform missions such as weather observation, communication relay, surveillance, and reconnaissance at 18km to 20km, where climate change is minimal and there is no worry about a collision with aircraft. It uses solar panels for daytime flights and energy stored in batteries for night flights, providing many advantages over existing satellites. The electrical and power systems essential for stratospheric drone flight must ensure reliability, efficiency, and lightness by selecting the optimal circuit topology. Therefore, it is necessary to analyze the circuit topology of various types of multi-level inverters with high redundancy that can ensure the reliability and efficiency of the motor driving power required for stable long-term flight of stratospheric drones. By quantifying the switch element voltage drop and the number and weight of inverter components for each topology, we evaluate efficiency and lightness and propose the most suitable circuit topology for stratospheric drones.

• Smart Structures and Systems
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• 제32권4호
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• pp.253-266
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• 2023

The time-reversal method is employed to improve the ability of pipeline defect detection, and a new approach of identifying the pipeline defect depth is proposed in this research. When the L(0,2) mode ultrasonic guided wave excited through a lead zirconate titinate (PZT) transduce array propagates along the pipeline with a defect, it will interact with the defect and be partially converted to flexural F(n, m) modes and longitudinal L(0,1) mode. Using a receiving PZT array attached axisymmetrically around the pipeline, the L(0,2) reflection signal as well as the mode conversion signals at the defect are obtained. An appropriate rectangle window is used to intercept the L(0,2) reflection signal and the mode conversion signals from the obtained direct detection signals. The intercepted signals are time reversed and re-excited in the pipeline again, result in the guided wave energy focusing on the pipeline defect, the L(0,2) reflection and the L(0,1) mode conversion signals being enhanced to a higher level, especially for the small defect in the early crack stage. Besides the L(0,2) reflection signal, the L(0,1) mode conversion signal also contains useful pipeline defect information. It is possible to identify the pipeline defect depth by monitoring the variation trend of L(0,2) and L(0,1) reflection coefficients. The finite element method (FEM) simulation and experiment results are given in the paper, the enhancement of pipeline defect reflection signals by time-reversal method is obvious, and the way to identify pipeline defect depth is demonstrated to be effective.

• 대한조선학회논문집
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• 제60권5호
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• pp.367-374
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• 2023

As the Fourth Industrial Revolution accelerating, countries worldwide are developing technologies to digitize and automate various industrial sectors. Building smart factories not only reduces costs through improved process productivity but also allows for preemptive identification and removal of risk factors through the practice of Health, Safety, and Environment (HSE) management, thereby reducing industrial accident risks. In this study, we visualized pressure, temperature, power, and wind speed data measured in real-time via a monitoring GUI, enabling field managers and workers to easily access related information. Through the work environment monitoring system developed in this study, it is possible to conduct economic analysis on per-unit basis, based on the digitization of production management elements and the tracking of required resources. By implementing HSE in shipyards, potential risk factors can be improved, and gas and electrical leaks can be identified, which are expected to reduce production costs.

• 한국구조물진단유지관리공학회 논문집
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• 제27권5호
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• pp.105-112
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• 2023

강섬유보강콘크리트는 일반 콘크리트에 비해 높은 강도 및 우수한 에너지 소산 능력을 보이며, 폭발하중 작용 시 균열 전파 및 파편 발생을 감소시킬 수 있다. 본 연구에서는 유한요소해석 프로그램인 LS-DYNA에 SFRC 재료물성을 구현하고자 콘크리트 비선형 재료모델인 K&C 모델의 파괴 곡면(Failure surface) 및 손상 함수(Damage function)를 정의하는 파라미터를 제안하였다. 제안 파라미터 검증을 위하여 단일요소해석을 수행하였으며, 제안 파라미터가 적용된 재료모델은 SFRC 재료시험 거동을 상당히 유사하게 모사하는 것으로 나타났다. 또한, 강섬유 혼입률에 따른 SFRC 기둥의 성능을 평가하기 위하여 내폭해석을 수행하였으며, KOSHA 규정을 참조하여 섬유 혼입률에 따른 SFRC 기둥의 내폭성능을 정량적으로 분석하였다.

• Smart Structures and Systems
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• 제33권1호
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• 한국전자통신학회논문지
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• 제19권1호
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• pp.209-216
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• 2024

육상 새우 양식장은 연안 양식에 비해 생장 환경을 보다 안정적으로 제어할 수 있어 고품질의 대규모 생산에 유리하다. 최적의 새우 생장 환경을 유지하기 위해서는 물의 순환, 적정 수온 유지, 산소 공급, 사료 공급 등 다양한 요소들을 관리해야 한다. 특히, 적절한 수질 관리가 되지 못하면 새우 폐사로 이어지기 때문에 양식장에 24시간 사람이 상주하여 지속적으로 관리해야 하는 어려움이 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 최소한의 인력으로 운영 가능한 육상 양식장 통합 모니터링 시스템을 제안한다. 제안된 시스템은 IoT 기술을 활용하여 육상 양식장의 실시간 영상, 펌프 상태, 수질 데이터, 에너지 사용 현황을 수집하여 서버로 전송한다. 관리자는 웹 인터페이스 및 스마트폰 앱을 통해 서버에 저장된 양식장의 상황을 언제 어디서나 실시간으로 확인하고 필요한 조치를 취할 수 있다. 따라서 관리자가 양식장에 상주할 필요 없이 현장 작업 시간을 크게 줄일 수 있다.

• Smart Structures and Systems
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• 제33권2호
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• pp.165-175
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• 2024

Rotating beams play a crucial role in representing complex mechanical components that are prevalent in vital sectors like energy and transportation industries. These components are susceptible to the initiation and propagation of cracks, posing a substantial risk to their structural integrity. This study presents a two-stage methodology for detecting the location and estimating the size of an open-edge transverse crack in a rotating Euler-Bernoulli beam with a uniform cross-section. Understanding the dynamic behavior of beams is vital for the effective design and evaluation of their operational performance. In this regard, modal parameters such as natural frequencies and eigenmodes are frequently employed to detect and identify damages in mechanical components. In this instance, the Frobenius method has been employed to determine the first two natural frequencies and corresponding eigenmodes associated with flapwise bending vibration. These calculations have been performed by solving the governing differential equation that describes the motion of the beam. Various parameters have been considered, such as rotational speed, beam slenderness, hub radius, and crack size and location. The effect of the crack has been replaced by a rotational spring whose stiffness represents the increase in local flexibility as a result of the damage presence. In the initial phase of the proposed methodology, a damage index utilizing the slope of the beam's eigenmode has been employed to estimate the location of the crack. After detecting the presence of damage, the size of the crack is determined using a Genetic Algorithm optimization technique. The ultimate goal of the proposed methodology is to enable the development of more suitable and reliable maintenance plans.

• Earthquakes and Structures
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• 제27권1호
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• pp.1-15
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• 2024

In order to enhance the seismic performance of base-isolated structures on soft foundations, the hybrid system of base-isolated system (BIS) and shape memory alloy inerter (SMAI), referred to as BIS+SMAI, is for the first time here proposed. Considering the nonlinear hysteretic relationships of both the isolation layer and SMA, and soil-structure interaction (SSI), the equivalent linearized state space equation is established of the structure-BIS+SMAI system. The displacement variance based on the H₂ norm is then formulated for the structure with BIS+SMAI. Employing the particle swarm optimization, the optimization design methodology of BIS+SMAI is presented in the frequency domain. The evolvement rules of BIS+SMAI in the effectiveness, robustness, SMA driving force, inertia force, stroke, and damping enhancement effect are revealed in the frequency domain through changing the inerter-mass ratio, structural height, aspect ratio, and relative stiffness ratio between the soil and structure. Meanwhile, the validation of BIS+SMAI is conducted using real earthquake records. Results demonstrate that BIS+SMAI can effectively reduce the isolation layer displacement. The inerter can significantly increase the hysteretic displacement of SMA and thus enhance its energy dissipation capacity, implying that BIS+SMAI has better effectiveness than BIS+SMA. Although BIS+SMAI and BIS+ tuned inerter damper (TID) have practically the same effectiveness, BIS+SMAI has the lower optimum damping, significantly smaller inertia force, and higher robustness to perturbations of the optimum parameters. Therefore, BIS+SMAI can be used as a more engineering realizable hybrid system for enhancing the performance of base-isolated structures in soft soil areas.

• Smart Structures and Systems
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• 제33권5호
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.