• Smart Structures and Systems
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• v.3 no.2
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• pp.133-146
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• 2007

This paper addresses a new type of broad and stage-based hybrid carbon fiber reinforced polymer (HCFRP) sensor that is suitable for the sensing of infrastructures. The HCFRP sensors, a type of composite sensor, are fabricated with three types of carbon tows of different strength and moduli. For all of the specimens, the active materials are carbon tows by virtue of their electrical conductivity and piezoresistivity. The measurement principles are based on the micro- and macro-fractures of different types of carbon tows. A series of experiments are carried out to investigate the sensing performances of the HCFRP sensors. The main variables include the stack order and volume fractions of different types of carbon tows. It is shown that the change in electrical resistance is in direct proportion to the strain/load in low strain ranges. However, the fractional change in electrical resistance (${\Delta}R/R_0$) is smaller than 2% prior to the macrofractures of carbon tows. In order to improve the resistance changes, measures are taken that can enhance the values of ${\Delta}R/R_0$ by more than 2 times during low strain ranges. In high strain ranges, the electrical resistance changes markedly with strain/load in a step-wise manner due to the gradual ruptures of different types of carbon tows at different strain amplitudes. The values of ${\Delta}R/R_0$ due to the fracture of high modulus carbon tows are larger than 36%. Thus, it is demonstrated that the HCFRP sensors have a broad and stage-based sensing capability.

• Smart Structures and Systems
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• v.13 no.1
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• pp.55-80
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• 2014

The present work pays emphasis on investigating the effect of different types of debonding on the bending behaviour of active sandwich beam, consisting of both extension and shear actuators. An active sandwich beam finite element is formulated by using Timoshenko's beam theory, characterized by first order shear deformation for the core and Euler-Bernoulli's beam theory for the top and bottom faces. The problem of debondings of extension actuator and face are dealt with by employing four-region model for inner debonding and three-region model for the edge debonding respectively. Displacement based continuity conditions are enforced at the interfaces of different regions using penalty method. Firstly, piezoelectric actuation of healthy sandwich beam is assessed through deflection analysis. Then the effect of actuators' debondings with different boundary conditions on bending behavior is computationally evaluated and experimentally clamped-free case is validated. The results generated will be useful to address the damage tolerant design procedures for smart sandwich beam structures with structural control and health monitoring applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.96-98
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• 2014

Wireless Sensor Network(WSN) is a wireless real-time information(Acquired from the sensor nodes that have the computing power and wireless communication capabilities.) collected, and to take advantage of processing techniques. Currently it is very diverse, such as environmental monitoring, health care, security, smart home, smart grid applications is that. Thus it is required in the wireless sensor network, the algorithm for the efficient use of the limited energy capacity. Suggested by the algorithm for selecting a cluster head node for a hybrid type and clustered, by comparing the amount of energy remaining and a connection between the nodes In this paper, we aim to increase efficiency and accuracy of the wireless sensor network.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.340-351
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• 2023

Wearable sensors designed for strain, pressure, and temperature measurements are essential for monitoring human movements, health status, physiological data, and responses to external stimuli. Notably, recent research has led to the development of high-performance wearable sensors using innovative materials and device structures that exhibit ultra-high sensitivity compared with their commercial counterparts. However, the quest for accurate sensing has identified a critical challenge. Specifically, the mechanical flexibility of the substrates in wearable sensors can introduce interference signals, particularly when subjected to varying external stimuli and environmental conditions, potentially resulting in signal crosstalk and compromised data fidelity. Consequently, the pursuit of non-interference sensing technology is pivotal for enabling independent measurements of concurrent input signals related to strain, pressure, and temperature, ensuring precise signal acquisition. In this comprehensive review, we present an overview of the recent advances in noninterference sensing strategies. We explore various fabrication methods for sensing strain, pressure, and temperature, emphasizing the use of hybrid composite materials with distinct mechanical properties. This review contributes to the understanding of critical developments in wearable sensor technology that are vital for their ongoing application and evolution in numerous fields.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.599-610
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• 2021

Early detection of small concrete crack or reinforcement corrosion is necessary for Structural Health Monitoring (SHM). Global vibration based methods are advantageous over local methods because of simple equipment installation and cost efficiency. Among vibration based techniques, FRF based methods are preferred over modal based methods. In this study, a new coupled method using frequency response function (FRF) and proper orthogonal modes (POM) is proposed by using the dynamic characteristic of a damaged beam. For the numerical simulation, wave finite element (WFE), coupled with traditional finite element (FE) method is used for effectively incorporating the damage related information and faster computation. As reported in literature, hybrid combination of wave function based wave finite element method and shape function based finite element method can addresses the mid frequency modelling difficulty as it utilises the advantages of both the methods. It also reduces the dynamic matrix dimension. The algorithms are implemented on a three-dimensional reinforced concrete beam. Damage is modelled and studied for two scenarios, i.e., crack in concrete and rebar corrosion. Single and multiple damage locations with different damage length are also considered. The proposed methodology is found to be very sensitive to both single- and multiple- damage while being computationally efficient at the same time. It is observed that the detection of damage due to corrosion is more challenging than that of concrete crack. The similarity index obtained from the damage parameters shows that it can be a very effective indicator for appropriately indicating initiation of damage in concrete structure in the form of spread corrosion or invisible crack.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of Ocean Engineering and Technology
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• v.38 no.2
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• pp.64-73
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• 2024

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.258-270
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• 2016

This study was conducted to test the green-tide mitigation technique in the lower part of the Sook Stream (Chusori) of Daecheong Reservoir from June 27 to August 24, 2014. And the effects were compared with weekly monitoring result of the watching station of the algae alert system (AAS) as well as test beds reach. The green-tide in a test bed was begun from the upstream, and it was gradually transferred and spread toward the downstream by the hydrological factors. The total amount of algae removed by algae removal device during the test period was 33,920 kg, and solids dewatered by natural gravity was 8,480 kg. Also chlorophyll-a content was 2.83 kg, the number of blue-green algae cells was equivalent to $78.6{\times}10^{14}$ cells. Compared with the results of the watching station of AAS, the pre-concentrate removal work in the outbreak waters was able to suggest the possibility of green-tide mitigation. In addition, an effective management of the green-tide was required spatial and temporal occurrence information and practical device technology. Particularly, the optimal timing of algae removal in the river-reservoir hybrid system was recommended at times before the monsoon rainy season and reached the lowest water level.