• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.4
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• pp.347-355
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• 2013

A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.

• Smart Structures and Systems
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• v.4 no.1
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• pp.47-66
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• 2008

The present work utilizes system identification technique for health monitoring of shear building, wherein Parametric State Space modeling has been adopted. The method requires input excitation to the structure and also output acceleration responses of both undamaged and damaged structure obtained from numerically simulated model. Modal parameters like eigen frequencies and eigen vectors have been extracted from the State Space model after introducing appropriate transformation. Least square technique has been utilized for the evaluation of the stiffness matrix after having obtained the modal matrix for the entire structure. Highly accurate values of stiffness of the structure could be evaluated corresponding to both the undamaged as well as damaged state of a structure, while considering noise in the simulated output response analogous to real time scenario. The damaged floor could also be located very conveniently and accurately by this adopted strategy. This method of damage detection can be applied in case of output acceleration responses recorded by sensors from the actual structure. Further, in case of even limited availability of sensors along the height of a multi-storeyed building, the methodology could yield very accurate information related to structural stiffness.

• Smart Structures and Systems
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• v.15 no.3
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• pp.807-830
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• 2015

When a building structure requires both health monitoring system and vibration control system, integrating the two systems together will be cost-effective and beneficial for creating a smart building structure with its own sensors (nervous system), processors (brain system), and actuators (muscular system). This paper presents a real-time integrated procedure to demonstrate how health monitoring and vibration control can be integrated in real time to accurately identify time-varying structural parameters and unknown excitations on one hand, and to optimally mitigate excessive vibration of the building structure on the other hand. The basic equations for the identification of time-varying structural parameters and unknown excitations of a semi-active damper-controlled building structure are first presented. The basic equations for semi-active vibration control of the building structure with time-varying structural parameters and unknown excitations are then put forward. The numerical algorithm is finally followed to show how the identification and the control can be performed simultaneously. The results from the numerical investigation of an example building demonstrate that the proposed method is feasible and accurate.

• Structural Engineering and Mechanics
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• v.30 no.2
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• pp.191-209
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• 2008

A structural monitoring system based on cheap and wireless monitoring system is investigated in this paper. Due to low-cost and low power consumption, micro-electro-mechanical system (MEMS) is suitable for wireless monitoring and the use of MEMS and wireless communication can reduce system cost and simplify the installation for structural health monitoring. For system identification using wireless MEMS, a finite element (FE) model updating method through correlation with the initial analytical model of the structure to the measured one is used. The system identification using wireless MEMS is evaluated experimentally using a three storey frame model. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS estimates system parameters with reasonable accuracy. Another smart sensor considered in this paper for structural health monitoring is Lead Zirconate Titanate (PZT) which is a type of piezoelectric material. PZT patches have been applied for the health monitoring of structures owing to their simultaneous sensing/actuating capability. In this paper, the system identification for building structures by using PZT patches functioning as sensor only is presented. The FE model updating method is applied with the experimental data obtained using PZT patches, and the results are compared to ones obtained using wireless MEMS system. Results indicate that sensing by PZT patches yields reliable system identification results even though limited information is available.

• Smart Structures and Systems
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• v.17 no.1
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.2
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• pp.64-73
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• 2014

This study proposes a load identification for the safety monitoring of the steel structure based on measured strain data. Instead of parameterizing the stiffness of structure in the existing system identification researches, the loads on a structure and a matrix (the unit strain matrix) defined by the relationship between strain and load on structure are parameterized in this study. The error function is defined by the difference between measured strain and strain estimated by parameters. In order to minimize this error function, the genetic algorithm which is one of the optimization algorithm is applied and the parameters are found. The loads on the structure can be identified through the founded parameters and measured strain data. When the loads are changed, the unmeasured strains are estimated based on founded parameters and measured strains on changed state of structure. To verify the load identification algorithm in this paper, the static experimental test for 3 dimensional steel frame structure was implemented and the loads were exactly identified through the measured strain data. In case of loading changes, the unmeasured strains which are monitoring targets on the structure were estimated in acceptable error range (0.17~3.13%). It is expected that the identification method in this study is applied to the safety monitoring of steel structures more practically.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.51-58
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• 2020

In this study, a novel method for sweeping automatic linearization of wavelength swept laser is proposed. Through the test performed on the implemented laser, the linear sweeping is held up well with a 97% decrease in nonlinearity, and 60 nm sweeping range, 1 kHz sweeping frequency, and 8.8 mW average optical power were obtained. The proposed method uses fiber Bragg grating array, optical-electronic conversion circuit, FPGA embedded module, and a LabVIEW program to generate new compensated wave patterns which were applied to the fiber Fabry-Perot tunable filter. Linear sweeping can reduce the cumbersome and time-consuming recalibration process required for nonlinear sweeping. Additionally, the proposed method provides more accurate measurement results for the structure safety monitoring system.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.894-900
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• 2009

In this paper, we propose the structure of system integration on the global ethernet networks using OpenRTM which was developed in Japan among the integration software framework based on middleware. In order to verify for propose model, we implement of environment monitoring system of mobile robot applied OpenRTM structure and we also verify that proposed validity of the structure of integration through experiment in the implemented system.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.147-155
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• 2010

It often occur to nature disaster that like earthquake, typhoon, etc. around KOREA. A Haiti and Chile also metropolitan area of KOREA occur earthquake. in result, People consider nature disaster. Structures of present age are easily affected by nature disaster. So we are important that warn of dangerous situation as soon as possible. On this study, I introduce Integrated Health Monitoring System for Infra-structure. I develop Structure Health Monitoring System on web-site. Administrator always monitor structure on real-time using internet network. As Administrator using mobile device like PDA, Administrator always monitor structure. As using this system, Damage of nature disaster is minimized and is prevented post damage.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.