• Computers and Concrete
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• v.20 no.5
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• pp.545-553
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• 2017

A pounding tuned mass damper (PTMD) can be considered as a passive device, which combines the merits of a traditional tuned mass damper (TMD) and a collision damper. A recent analytical study by the authors demonstrated that the PTMD base on the energy dissipation during impact is able to achieve better control effectiveness over the traditional TMD. In this paper, a PTMD prototype is manufactured and applied for seismic response reduction to examine its efficacy. A series of shaking table tests is conducted in a three-story building frame model under single-dimensional and two-dimensional broadband earthquake excitations with different excitation intensities. The ability of the PTMD to reduce the structural responses is experimentally investigated. The results show that the traditional TMD is sensitive to input excitations, while the PTMD mostly has improved control performance over the TMD to remarkably reduce both the peak and root-mean-square (RMS) structural responses under single-dimensional earthquake excitation. Unlike the TMD, the PTMD is found to have the merit of maintaining a stable performance when subjected to different earthquake loadings. In addition, it is also indicated that the performance of the PTMD can be enhanced by adjusting the initial gap value, and the control effectiveness improves with the increasing excitation intensity. Under two-dimensional earthquake inputs, the PTMD controls remain outperform the TMD controls; however, the oscillation of the added mass is observed during the test, which may induce torsional vibration modes of the structure, and hence, result in poor control performance especially after a strong earthquake period.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.87-94
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• 2012

A damage estimation method of wind turbine tower using natural frequency and mode shape is presented for effective condition monitoring. Dynamic analysis for a wind turbine was carried out to obtain the response of tower from which modal properties were identified. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. The changes of modal property were calculated using a program for modal parameter estimation. Damage locations and severities could be successfully estimated for 10 damage cases including multi-damage cases using the trained neural network. The damage severities for very small damages generally tends to be slightly under-estimated however, the identified damage locations agreed reasonably well with the accurate locations. Enhancement of the estimation result for very small damage and verification of the proposed method through experiment will be carried out by further study.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.749-765
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• 2010

There are on-going efforts to utilize guided waves for structural damage detection. Active sensing devices such as lead zirconate titanate (PZT) have been widely used for guided wave generation and sensing. In addition, there has been increasing interest in adopting wireless sensing to structural health monitoring (SHM) applications. One of major challenges in wireless SHM is to secure power necessary to operate the wireless sensors. However, because active sensing devices demand relatively high electric power compared to conventional passive sensors such as accelerometers and strain gauges, existing battery technologies may not be suitable for long-term operation of the active sensing devices. To tackle this problem, a new wireless power transmission paradigm has been developed in this study. The proposed technique wirelessly transmits power necessary for PZT-based guided wave generation using laser and optoelectronic devices. First, a desired waveform is generated and the intensity of the laser source is modulated accordingly using an electro-optic modulator (EOM). Next, the modulated laser is wirelessly transmitted to a photodiode connected to a PZT. Then, the photodiode converts the transmitted light into an electric signal and excites the PZT to generate guided waves on the structure where the PZT is attached to. Finally, the corresponding response from the sensing PZT is measured. The feasibility of the proposed method for wireless guided wave generation has been experimentally demonstrated.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.5
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• pp.150-157
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• 2020

In this study, an algorithm was developed to validate the seismic acceleration measurement data of the seismic acceleration measurement system using measurement data from public buildings currently in operation. Through the results of the study, an algorithm was developed to detect errors and abnormalities in the measurement data itself and the process of generating real-time data (MMA/sec) and event measurement data (MiniSEED), which are the main data generated by the system, and the basic data for determining the direction of inspection through measurement data analysis. It is expected that this will be used as a guideline to determine whether or not the seismic acceleration measurement system, which was managed as receiving/not receiving, is inspected and abnormal types of conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.81-88
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• 2021

Structural health monitoring (SHM) systems have attracted considerable interest owing to the frequent earthquakes over the last decade. Smart concrete is a technology that can analyze the state of structures based on their electro-mechanical behavior. On the other hand, most research on the self-sensing response of smart concrete generally investigated the electro-mechanical behavior of smart concrete under a static loading rate, even though the loading rate under an earthquake would be much faster than the static rate. Thus, this study evaluated the electro-mechanical behavior of smart ultra-high-performance concrete (S-UHPC) at three different loading rates (1, 4, and 8 mm/min) using a Universal Testing Machine (UTM). The stress-sensitive coefficient (SC) at the maximum compressive strength of S-UHPC was -0.140 %/MPa based on a loading rate of 1 mm/min but decreased by 42.8% and 72.7% as the loading rate was increased to 4 and 8 mm/min, respectively. Although the sensing capability of S-UHPC decreased with increased load speed due to the reduced deformation of conductive materials and increased microcrack, it was available for SHM systems for earthquake detection in structures.

• Journal of Korean Society of Steel Construction
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• v.17 no.1 s.74
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• pp.1-11
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• 2005

A bi-level damage detection algorithm that utilizes the dynamic responses of the structure as input and neural network (NN) as pattern classifier is presented. Signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRF) or strain frequency response function (SFRF). SAI is calculated using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, the presence of damage is first identified from the magnitude of the SAI value, then the location of the damage is identified using the pattern recognition capability of NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally-acquired signals are used to test the NN. The results of this example application suggest that the SAI-based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.

• Smart Structures and Systems
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• v.12 no.1
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• pp.55-71
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• 2013

Structural health monitoring (SHM) is vital for detecting the onset of damage and for preventing catastrophic failure of civil infrastructure systems. In particular, piezoelectric transducers have the ability to excite and actively interrogate structures (e.g., using surface waves) while measuring their response for sensing and damage detection. In fact, piezoelectric transducers such as lead zirconate titanate (PZT) and poly(vinylidene fluoride) (PVDF) have been used for various laboratory/field tests and possess significant advantages as compared to visual inspection and vibration-based methods, to name a few. However, PZTs are inherently brittle, and PVDF films do not possess high piezoelectricity, thereby limiting each of these devices to certain specific applications. The objective of this study is to design, characterize, and validate piezoelectric nanocomposites consisting of zinc oxide (ZnO) nanoparticles assembled in a PVDF copolymer matrix for sensing and SHM applications. These films provide greater mechanical flexibility as compared to PZTs, yet possess enhanced piezoelectricity as compared to pristine PVDF copolymers. This study started with spin coating dispersed ZnO- and PVDF-TrFE-based solutions to fabricate the piezoelectric nanocomposites. The concentration of ZnO nanoparticles was varied from 0 to 20 wt.% (in 5 % increments) to determine their influence on bulk film piezoelectricity. Second, their electric polarization responses were obtained for quantifying thin film remnant polarization, which is directly correlated to piezoelectricity. Based on these results, the films were poled (at 50 $MV-m^{-1}$) to permanently align their electrical domains and to enhance their bulk film piezoelectricity. Then, a series of hammer impact tests were conducted, and the voltage generated by poled ZnO-based thin films was compared to commercially poled PVDF copolymer thin films. The hammer impact tests showed comparable results between the prototype and commercial samples, and increasing ZnO content provided enhanced piezoelectric performance. Lastly, the films were further validated for sensing using different energy levels of hammer impact, different distances between the impact locations and the film electrodes, and cantilever free vibration testing for dynamic strain sensing.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.139.2-139.2
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• 2016

Electrons and phonons in chalcogenide-based materials play are important factors in the performance of an optical data storage media and thermoelectric devices. However, the fundamental kinetics of carriers in chalcogenide materials remains controversial, and active debate continues over the mechanism responsible for carrier relaxation. In this study, we investigated ultrafast carrier dynamics in an multilayered $\{Sb(3{\AA})/Te(9{\AA})\}n$ thin film during the transition from the amorphous to the crystalline phase using optical pump terahertz probe spectroscopy (OPTP), which permits the relationship between structural phase transition and optical property transitions to be examined. Using THz-TDS, we demonstrated that optical conductance and carrier concentration change as a function of annealing temperature with a contact-free optical technique. Moreover, we observed that the topological surface state (TSS) affects the degree of enhancement of carrier lifetime, which is closely related to the degree of spin-orbit coupling (SOC). The combination of an optical technique and a proposed carrier relaxation mechanism provides a powerful tool for monitoring TSS and SOC. Consequently, the response of the amorphous phase is dominated by an electron-phonon coupling effect, while that of the crystalline structure is controlled by a Dirac surface state and SOC effects. These results are important for understanding the fundamental physics of phase change materials and for optimizing and designing materials with better performance in optoelectronic devices.

• Asian-Australasian Journal of Animal Sciences
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• v.4 no.1
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• pp.91-97
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• 1991

Twenty five 75% Holstein Friesian cross bred bullocks fed rice straw (Oryza sativa) of long form, were fed with the following five treatments. 1. Rice straw, untreated (RS) 2. RS + water (1:1), stored for 24 hours (WRS) 3. RS (100 kg) + urea solution (4 kg urea/100 litre water) and dried (USRS) 4. RS (100 kg) + urea solution (as in 3) stored in wet condition for 24 hours (UWRS) 5. RS (100 kg) + urea solution (as in 3) stored in pit for 21 days (UTRS). Potential digestibility of treatments of RS was evaluated by monitoring (in vitro) Simulating Rumen like Fermentation (SRLF). The results indicated that Dry Matter Intake (DMI), digestibility of nutrients, N utilization were of the order UTRS > UWRS > USRS > WRS and RS (p < 0.05 to p < 0.01). SRLF index was high (255.84) for UTRS and least (145.58) for USRS. It was intermediary (199.66) for UWRS. The acetyl content (AC) of UTRS with higher hemicellulose (HCE) digestibility (80.8%) was low compared to UWRS, USRS, RS and WRS. The acetate content was of the order UTRS < UWRS < USRS < WRS and RS thereby indicating that reduction in acetyl content was an index of positive response of urea-treatment of RS. In addition, the ratio of HCE/AC in faeces of UTRS was 0.87 as against the ratios (2.26-2.48) observed in other treatments recording reduction in AC due to urea-treatment. Among the treatments, USRS only supplemented N while UTRS in addition to utilization N, increased the digestibility of structural carbohydrates. Reduction in treatment time from 21 days to 1 day (UWRS) resulted in improvements similar to those of UTRS.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.