• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.513-520
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• 2005

An active modal-fuzzy control method using hydraulic actuators is presented for seismic response reduction. In the proposed control system, a new fuzzy controller designed in the modal space produces the desired active control force. This type controller has all advantages of the fuzzy control algorithm and modal approach. Since it is very difficult to select input variables used in fuzzy controller among an amount of state variables in the active fuzzy control system, the presented algorithm adopts the modal control algorithm which is able to consider more easily information of all state variables in civil structures that are usually dominated by first few modes. In other words, all information of the whole structure can be considered in the control algorithm evaluated to reduce seismic responses and it can be efficient for especially civil structures. In addition, the presented algorithm is expected to magnify utility and performance caused by efficiency that the fuzzy algorithm can handle complex model more easily. An active modal-fuzzy control scheme is applied together with a Kalman filter and a low-pass filter to be applicable to real civil structures. A Kalman filter is considered to estimate modal states and a low-pass filter was used to eliminate spillover problem. The results of the numerical simulations for a wide amplitude range of loading conditions show that the proposed active modal-fuzzy control system can be beneficial in reducing seismic responses of civil structures.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• Computational Structural Engineering
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• v.2 no.4
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• pp.111-116
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• 1989

Methods for the estimation of the coefficient matrices in the equation of motion for a linear multi-degree-of-freedom structure are studied. For this purpose, the equation of motion is transformed into an auto-regressive and moving average with auxiliary input(ARMAX) model. The ARMAX parameters are evaluated using several methods of parameter estimation : such as the least squares, the instrumental variable, the maximum likelihood and the limited information maximum likelihood methods. Then the parameters of the equation of motion are recovered therefrom. Numerical example is given for a 3-story building model subjected to an earthquake exitation.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.268-274
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• 2023

Finite source inversion is performed with a Green's function matrix and geodetic coseismic displacement. Conventionally, the Green's function matrix is constructed using the Okada model (Okada, 1985). However, for more realistic earthquake simulations, recent research has widely adopted the physics-based model, which can consider various material properties such as elasticity, viscoelasticity, and elastoplasticity. We used the physics-based software PyLith, which is suitable for earthquake modeling. However, the PyLith does not provide a mesh generator, which makes it difficult to perform finite source inversions that require numerous subfaults and observation points within the model. Therefore, in this study, we developed CPInterface (COMSOL-PyLith Interface) to improve the convenience of finite source inversion by combining the processes of creating a numerical model including sub-faults and observation points, simulating earthquake modeling, and constructing a Green's function matrix. CPInterface combines the grid generator of COMSOL with PyLith to generate the Green's function matrix automatically. CPInterface controls model and fault information with simple parameters. In addition, elastic subsurface anomalies and GPS observations can be placed flexibly in the model. CPInterface is expected to enhance the accessibility of physics-based finite source inversions by automatically generating the Green's function matrix.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.87-94
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• 1995

In highway bridges robust damage detection exercises are mandatory to secure the safety of the structures from hostile environmental conditions such as fatigue earthquake, wind, and corrosion. This paper presents a damage detection practice in a full-scale highway bridge by utilizing modal response parameters of as-built and damaged states of the structure. first the test structure is described and modal testing procedures are outlined. Next, a damage detection model which yields information on the location of damage directly from changes in mode shapes is outlined. Finally, the damage detection model is implemented to predict the location of damage in the ten structure. From the results, it was found that the damage detection model accurately locates damage in the test structures for which modal parameters of only a single mode are available for pre-damage (as-built) and post-damage stages.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.1-6
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• 2005

The purpose of this paper is to evaluate the performance of an MR fluid damper for seismic vibration control of a structure in terms of equivalent linear damping based on linearization technique and to experimentally verify the results from linearization technique by comparing them to those from system identification testing of a building structure with the MR damper. First, among various models for the MR damper, the equivalent damping is estimated for the Bingham model which is mathematically simple. Second, the transfer function of a building structure with the MR damper is obtained by performing shaking table tests and the damping matrices of the structure are constructed using the modal information obtained by the transfer function. It is observed that the damping mathematically estimated using linearization technique for the Bingham model matches well with the damping coefficient experimentally obtained by system identification.

• Journal of the Korean Geophysical Society
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• v.5 no.4
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• pp.305-319
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• 2002

The three-dimensional MT(Magneto-Telluric) modeling is performed to examine the validity of difference arrow of GDS(Geomagnetic Depth Sounding) survey, In this paper, we investigate the validity of the difference arrow on three configurations of conductors; which is located 1) at surface, 2) at the deep part and 3) vertically extended f개m surface to the deep part, respectively, For conductors located at surface, the validity of difference arrows is certified in our numerical model when long periods over 40 minutes are used or the distance between sea and conductor is over 150 km. However, for conductors located at the deep part, the validity of difference arrow is dependent on the size of conductors. Further, if the size of conductor is adequately larger than that of our model, we recognize the possibility that the mutual coupling of them influences up to longer periods, Moreover, in case of conductors which is vertically extended from surface to the deer part, the mutual coupling of them is reinforced for all periods, especially for longer periods, so that the validity of difference arrow is considerably in doubt. Therefore, to remove the known conductor effect such as the sea effect from the observed induction arrow, the mutual coupling between them must be examined. The difference arrow that certifies the validity in this way can only provide the Subsurface information based on physical supports.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.