• Smart Structures and Systems
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• v.20 no.3
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• pp.285-301
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• 2017

This paper presents the design and the application of a new self-powered hybrid electromagnetic damper that can harvest energy while mitigating the vibration of a structure. The damper is able to switch between an energy harvesting passive mode and a semi-active mode depending on the amount of energy harvested and stored in the battery. The energy harvested in the passive mode resulting from the suppression of vibration is employed to power up the monitoring and electronic components necessary for the semi-active control. This provides a hybrid control capability that is autonomous in terms of its power requirement. The proposed hybrid circuit design provides two possible options for the semi-active control: without energy harvesting and with energy harvesting. The device mechanism and the circuitry that can drive this self-powered electromagnetic damper are described in this paper. The parameters that determine the device feasible force-velocity region are identified and discussed. The effectiveness of this hybrid damper is evaluated through a numerical simulation study on vibration mitigation of a bridge stay cable under wind excitation. It is demonstrated that the proposed hybrid design outperforms the passive case without external power supply. It is also shown that a broader force range, facilitated by decoupled passive and semi-active modes, can improve the vibration performance of the cable.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.269-281
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• 2019

This work addresses a three-dimensional nonlinear structural analysis of the constructive phases of a cable-stayed segmental concrete bridge using The Finite Element Method through ANSYS, version 14.5. New subroutines have been added to ANSYS via its UPF customization tool to implement viscoelastoplastic constitutive equations with cracking capability to model concrete's structural behavior. This numerical implementation allowed the use of three-dimensional twenty-node quadratic elements (SOLID186) with the Element-Embedded Rebar model option (REINF264), conducting to a fast and efficient solution. These advantages are of fundamental importance when large structures, such as bridges, are modeled, since an increasing number of finite elements is demanded. After validating the subroutines, the bridge located in Rio de Janeiro, Brazil, and known as "Ponte do Saber" (Bridge of Knowledge, in Portuguese), has been numerically modeled, simulating each of the constructive phases of the bridge. Additionally, the data obtained numerically is compared with the field data collected from monitoring conducted during the construction of the bridge, showing good agreement.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.4
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• pp.333-338
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• 2017

In recent years, there has been an increasing demand for healthcare services that can periodically monitor health status and maintain health by increasing the weight training population. However, injuries in the absence of trainer are increasing with the increase in the number of members in the fitness training center. Therefore, there is a need for a system that can periodically monitor the user's exercise state and assist in systematic and safe exercise even when the trainer is absent. In this study, we developed an auto training system that can effectively manage the exerciser while supporting the strength movement. The auto training system consists of a cable mount module, a control module, and a training information monitoring module. In order to evaluate performance of the developed system, the assistant force tests are carried out. Experimental results showed that the assistant force works well when the exerciser is out of power.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.77-82
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• 2017

In the splay manufacturing process, conveyor systems are widely used for conveying panels. In this conveyor, a large number of grounding lines are used in order to prevent a product failure due to static electricity. In many cases, the grounding line is disconnected due to the rotation of the transporting roller or curling, leading to product failure. In order to solve such a problem, there is a growing need for a system capable of detecting disconnection of a ground wire in real time. Therefore, in this paper, we propose a disconnection monitoring system of ESD (Electro-Static Discharge) ground wire caused by friction between the conveyor drive part and the panel. The proposed system is a monitoring system that can detect disconnection and disconnection of ground wire using ATmega 2560 and Wheatstone Bridge circuit. It can detect disconnection of ground wire immediately and can take measures to reduce the defect rate due to static electricity. The system proposed in this paper is expected to be applicable to the production and test equipments of all industries where the ground wire is used.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1A
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• pp.1-10
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• 2012

Along with the development of coasts, islands and mountains, the demand of long-span bridges increases which, in turn, brings forth the construction of cable-supported bridges like suspension and cable-stayed bridges. There are various types of statically indeterminate structures widely applied that supported the main girder with stay cables, main cables, hanger cables with aesthetic structural appearance. As to the cable-supported bridges, the health monitoring of a bridge can be identified by measuring tension force on cable repeatedly. The tension force on cable is measured either by direct measurement of stress of cable using load cell or hydraulic jack, or by vibration method estimating tension force using cable shape and measured dynamic characteristics. In this study, a method to estimate dynamic characteristics of hanger cables by using a digital image processing is suggested. Digital images are acquired by a portable digital camcorder, which is the sensor to remotely measure dynamic responses considering convenient and economical aspects for use. A digital image correlation(DIC) technique is applied for digital image processing, and an image transform function(ITF) to correct the geometric distortion induced from the deformed images is used to estimate subpixel. And, the correction of motion of vision-based measurement system using a fixed object in an image without installing additional sensor can be enhanced the resolution of dynamic responses and modal frequencies of hanger cables.

• Smart Structures and Systems
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• v.2 no.3
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.743-746
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• 2011

An implementation for surface EMG network analysis and vertical control system of robotic arm is presented in this paper. The transmembranes are simulated by equivalent circuit and cable equation for propagation to be converted to circuit networks. The implementation is realized to be derived from the detecting EMG signal from 3 electrodes, and EMG transmembrane signals of human arm muscles are detected by several surface electrodes, high performance amplifier and filtering, converting analog to digital data and driving a servomotor for spontaneous robotic arm. The system is experimented by monitoring multiple steps vertical control angles corresponding to biceps muscle movement. The experimental results are that the vertical moving control level is measured to around 2 degrees and mean error ranges are lower 5%.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.15-38
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• 2018

Negative stiffness dampers (NSDs) have been proven an efficient solution to vibration control of stay cables. Although previous studies usually assumed a linear negative stiffness behavior of NSDs, many negative stiffness devices produce negative stiffness with nonlinear behavior. This paper systematically evaluates the impact of nonlinearity in negative stiffness on vibration control performance for stay cables. A linearization method based on energy equivalent principle is proposed, and subsequently, the impact of two types of nonlinear stiffness, namely, displacement hardening and softening stiffness, is evaluated. Through the Hilbert transform (HT) of free vibration responses, the effects of nonlinear stiffness of an NSD on the modal frequencies, damping ratios and frequency response functions of a stay cable is also investigated. The HT analysis results validate the accuracy of the linearization method.