• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.2
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• pp.1-9
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• 2013

In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

• Smart Structures and Systems
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• v.20 no.5
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.2
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• pp.165-170
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• 2014

Shear horizontal (SH) waves propagate as a type of plate wave in a thin sheet. The dispersion characteristics of SH waves can be used for signal analysis. Therefore, SH-waves are useful for monitoring the structural health of a thin-sheet-structure. An electromagnetic acoustic transducer (EMAT), which is a non-contact ultrasonic transducer, can generate SH-waves easily by varying the shape and array of magnets and coils. Therefore, an EMAT can be applied to an automated ultrasonic testing system for structural health monitoring. When used as a sensor, however, the EMAT has a weakness in that electromagnetic interference (EMI) noise can occur easily in the automated system because of motors and electric devices. Alternatively, a fiber optic sensor works well in the same environment with EMI noise because it uses a light signal instead of an electric signal. In this paper, a fiber Fabry-P$\acute{e}$rot interferometer (FFPI) was proposed as a sensor to receive the SH-waves generated by an EMAT. A simple test was performed to verify the performance of the FFPI sensor. It is thus shown that the FFPI can receive SH-wave signals clearly.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.77-84
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• 2020

In this paper, we developed a damage evaluation technique that can determine the damage location of a long-sized structure such as a cable-stayed bridge, and verified the performance of the developed technique through experiments. The damage assessment method aims to extract data that can evaluate the damage of the structure without the undamage data and can determine the damage location only by analyzing the response data of the structure. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To evaluate the performance of the developed technique experimentally, cable damage experiments were conducted on model cable-stayed bridges. As a result, the damage assessment method considering variability automatically outputs the damageless data according to external force, and it is confirmed that the performance of extracting information that can determine the damage location of the cable through the analysis of the outputted damageless data and the measured damage data is shown.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.1-7
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• 2023

An artificial intelligence (AI) method based on image deep learning is proposed to predict the entire displacement shape of a structure using the feature of partial displacements. The performance of the method was investigated through a structural test of a steel frame. An image-to-image regression (I2IR) training method was developed based on the U-Net layer for image recognition. In the I2IR method, the U-Net is modified to generate images of entire displacement shapes when images of partial displacement shapes of structures are input to the AI network. Furthermore, the training of displacements combined with the location feature was developed so that nodal displacement values with corresponding nodal coordinates could be used in AI training. The proposed training methods can consider correlations between nodal displacements in 3D space, and the accuracy of displacement predictions is improved compared with artificial neural network training methods. Displacements of the steel frame were predicted during the structural tests using the proposed methods and compared with 3D scanning data of displacement shapes. The results show that the proposed AI prediction properly follows the measured displacements using 3D scanning.

• Composites Research
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• v.36 no.6
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• pp.429-434
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• 2023

In this study, aimed at improving the existing acoustic emission sensor for real time monitoring, a macro-fiber composite (MFC) transducer was employed as the acoustic emission sensor in the gas leak detection system. Prior to implementation, structural analysis was conducted to optimize the MFC's design. Consequently, the flexibility of the MFC facilitated excellent adherence to curved pipes, enabling the reception of acoustic emission (AE) signals without complications. Analysis of AE signals revealed substantial variations in parameter values for both high-pressure and low-pressure leaks. Notably, in the parameters of the Fast Fourier Transform (FFT) graph, the change amounted to 120% to 626% for high-pressure leaks compared to the case without leaks, and approximately 9% to 22% for low-pressure leaks. Furthermore, depending on the distance from the leak site, the magnitude of change in parameters tended to decrease as the distance increased. As the results, in the future, not only will it be possible to detect a leak by detecting the amount of parameter change in the future, but it will also be possible to identify the location of the leak from the amount of change.

• Journal of Aquaculture
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• v.19 no.4
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• pp.275-280
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• 2006

Effect of the various sources of dietary additives on growth, body composition and shell color of abalone Haliotis discus hannai was investigated for 16 weeks. Forty juvenile abalone averaging 13.5 g were randomly stocked into 21 of 50 L plastic rectangular containers each. Eight kinds of additives were prepared for this study: four commercially available microalgae [Haeatococcus (Hae), Isochrysis galbana (Iso), Shizochytrium (Sch) and Spirulina (Spi)], three crustacean meals [krill meal (KM), shrimp head meal (Shm) and red crab meal (Rcm)], and green tea by-product (Gre). In addition, dry sea tangle (Dst), Laminaria japonica, as a control, was prepared. Casein, dextrin and a mixture corn oil and fish oil was protein, carbohydrate and lipid sources, respectively, in the experimental diets. The 2% each additive was included into the experimental diets. The experimental diets were fed to abalone once a day at the ratio of $1.5{\sim}2.0%$ total biomass of abalone with a little leftover throughout the 16-week feeding trial. Survival of abalone was not significantly (P>0.05) affected by the experimental diets. However, weight gain of abalone fed the all experimental diets containing the various sources of additives was significantly (P<0.05) higher than that of abalone fed the Dst diet. Weight gain of abalone fed the Spi diet was highest and Shi, KM and Iso diets in order. Shell length and the ratio of soft body weight to body weight of abalone was not significantly (P>0.05) affected by the experimental diets. However, shell width of abalone fed the all experimental diets containing the various sources of additives was significantly (P<0.05) higher than that of abalone fed the Dst diet. The shell color of abalone fed the Spi diet was improved the most distinctively and similar to that of natural abalone. Therefore, it can be concluded that the experimental diets with the various sources of additives (microalgae and crustacean meals) was effective to improve growth of abalone and dietary inclusion of Spirulina was most effective to improve shell color of abalone.