• Smart Structures and Systems
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• v.30 no.6
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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.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.63-70
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• 2011

A bicycle is different from vehicles in the structure that a rider is fully exposed to the surrounding environment. Therefore, it needs to make use of prior information about local weather, air quality, trail road condition. Moreover, since it depends on human power for moving, it should acquire route property such as hill slope, winding, and road surface to improve its efficiency in everyday use. Recent mobile applications which are to be used during bicycle riding let us aware of the necessity of development of intelligent bicycles. This study aims to develop a riding state (up-hill, down-hill, accelerating, braking) recognition algorithm using a low-power wrist watch type embedded system which has 3-axis accelerometer and wireless communication capability. The developed algorithm was applied to 19 experimental riding data and showed more than 95% of correct recognition over 83.3% of the total dataset. The altitude and temperature sensor also in the embedded system mounted on the bicycle is being used to improve the accuracy of the algorithm. The developed riding state recognition algorithm is expected to be a platform technology for intelligent bicycle interface system.

• Smart Structures and Systems
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• v.14 no.2
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• pp.209-224
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• 2014

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.6
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• pp.715-722
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• 2015

The prestressing force has not been managed after construction nevertheless it is one of the importrant factors that maintain the structural safety of PSC girder bridges. The prestressing force is just measured during construction using jacking device and after that, it can not be managed practically. For this reason, this study investigated the measurements of prestress using embedded sensors that can be available now with an ultimate goal to propose smart prestressed girders that can measure the prestress from the birth to the end of service life. 4 types of sensors were installed on the small prestressed girders, and the applicability and the accuracy of those sensors were tested while the prestress was applied to the girders. The results show that a center-hole type loadcell has a tendency to measure a prestressing force higher than a reference value, especially when it is loaded with an eccentricity. a EM sensor shows several advantages that has a good practical accuracy, that can be installed anyplace along the tendons.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.285-290
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• 2016

In rural area, our farmers confront decreasing benefits owing to imported crops and increased cost. Recently, the government encourage the 6th Industry that merges farming, rural resources, and information and communication technology. Therefor the government makes an investment in supplying 'smart greenhouse' in which a farmer monitor growing crops and environment information to control growing condition. The objective of this study is developing an Moving Monitor and Control System for crops in green House. This system includes a movable sensing unit, a controlling unit, and a server PC unit. The movable sensing unit contains high resolution IP camera, temperature and humidity sensor and WiFi repeater. It rolls on a rail hanging beneath the ceiling of a green house. The controlling unit contains embedded PC, PLC module, WiFi router, and BLDC motor to drive the movable sensing unit. And the server PC unit contains a integrated farm management software and home pages and databases in which the images of crops and environment informations. The movable sensing unit moves widely in a green house and gathers lots of information. The server saves these informations and provides them to customers with the direct commercing web page. This system will help farmers to control house environment and sales their crops in online market. Eventually It will be helpful for farmers to increase their benefits.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.651-662
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• 2022

The electrochemical impedance spectroscopy(EIS) method was used to evaluate the concrete deterioration process related to chloride-induced steel corrosion with various corrosion levels(initiation, rust propagation and acceleration periods). The impressed current technique, with four total current levels of 0C, 13C, 65C and 130C, was used to accelerate steel corrosion in concrete cylinder samples with w/c ratio of 0.4, 0.5, and 0.6, immersed in a 0.5M NaCl solution. A series of EIS measurements was performed to monitor concrete deterioration during the accelerated corrosion test in this study. Some critical parameters of the equivalent circuit were obtained through the EIS analysis. It was observed that the charge transfer resistance(R_c) dropped sharply as the impressed current increased from 0C to 13C, indicating a value of approximately 10kΩcm². However, the sensitivity of R_c significantly decreased when the impressed current was further increased from 13C to 130C after corrosion of steel had been initiated. Meanwhile, the double-layer capacitance value(C_dl) linearly increased from 50×10^-6μF/cm² to 250×10^-6μF/cm² as the impressed current in creased from 0C to 130C. The results in this study showed that monitoring C_dl is an effective measurement parameter for evaluating the progress of internal concrete damages(de-bonding between steel and concrete, micro-cracks, and surface-breaking cracks) induced by steel corrosion. The findings of this study provide a fundamental basis for developing an embedded sensor and signal interpretation method for monitoring concrete deterioration due to steel corrosion at various corrosion levels.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.82-92
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• 2022

This study aims to investigate the effect of total electrochemical cell resistance (TECR) on electrochemical impedance (EI) measurements of reinforced concrete (RC) by electrochemical impedance spectroscopy (EIS) using a three-electrode system. A series of experimental study is performed to measure electrochemical behavior of a steel bar embedded in a concrete cube specimen, with a side length of 200 mm, in various experimental conditions. Main variables include concrete dry conditions, coupling resistance between sensing electrodes and concrete surface, and area of the counter electrode. It is demonstrated that EI values remains stable when the compliant voltage of a measuring device is sufficiently great compared to the potential drop caused by TECR of concrete specimens. It is confirmed that the effect of the coupling resistance of TECR is far more influential than other two factors (concrete dry conditions and area of the counter electrode). The results in this study can be used as a fundamental basis for development of a surface-mount sensor for corrosion monitoring of reinforced concrete structures exposed to wet-and-dry cycles under marine environment.