• Coupled systems mechanics
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• v.12 no.5
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• pp.429-444
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• 2023

The comprehension and structural modeling of masonry constructions is fundamental to safeguard the integrity of built cultural assets and intervene through adequate actions, especially in earthquake-prone regions. Despite the availability of several modeling strategies and modern computing power, modeling masonry remains a great challenge because of still demanding computational efforts, constraints in performing destructive or semi-destructive in-situ tests, and material uncertainties. This paper investigates the shear behavior of masonry walls by applying a plane-stress FE continuum model with the Modified Masonry-like Material (MMLM). Epistemic uncertainty affecting input parameters of the MMLM is considered in a probabilistic framework. After appointing a suitable probability density function to input quantities according to prior engineering knowledge, uncertainties are propagated to outputs relying on gPCE-based surrogate models to considerably speed up the forward problem-solving. The sensitivity of the response to input parameters is evaluated through the computation of Sobol' indices pointing out the parameters more worthy to be further investigated, when dealing with the seismic assessment of masonry buildings. Finally, masonry mechanical properties are calibrated in a probabilistic setting with the Bayesian approach to the inverse problem based on the available measurements obtained from the experimental load-displacement curves provided by shear compression in-situ tests.

• Smart Structures and Systems
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• v.31 no.4
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• pp.393-407
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• 2023

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BI-SMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.

• Journal of the Korea Concrete Institute
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• v.21 no.3
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• pp.359-366
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• 2009

A new life-cycle cost (LCC) evaluation scheme for marine reinforced concrete structures is proposed. In this method, unlike the conventional life-cycle cost evaluation performed during the design process, the life-cycle cost is updated successively whenever new information of the chloride penetration is available. This updating is performed based on the Bayesian approach. For important structures, information required for this new method can be obtained without any difficulties because it is a common element of various types of monitoring systems. Using the new method, the life-cycle maintenance cost of structures can be estimated with a good precision.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1496-1499
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• 2004

The paper persents an efficient method of extracting line segment in a grid map. The grid map is composed of 2-D grids that have both the occupancy and orientation probabilities based on the simplified Bayesian updating model. The probabilities and orientations of cells in the grid map are continuously updated while the robot explorers to their values. The line segments are, then, extracted from the clusters using Hough transform methods. The eng points of a line segment are evaluated from the cells in each cluster, which is simple and efficient comparing to existing methods. The proposed methods are illustrated by sets of experiments in an indoor environment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.2
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• pp.139-146
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• 2013

This study is to update of vibration power generating model and to analyze electromotive force on subway. Analysis of electromotive force using power generation depending on classification of locations which are ballast bed and concrete bed. As the section between Seocho and Bangbae in the line 2 subway was changed from ballast bed to concrete bed, it could be analyzed at same condition, train, section. Induced electromotive force equation by Faraday's law was updated using Bayesian regression and correlation analysis with calculate value and experiment value. Using the updated model, it could get 40mV per one power generation in ballast bed, and it also could get 4mV per one power generation in concrete bed. If the updated model apply to subway or any train, it will be more effective to get electric power. In addition to that, it will be good to reduce greenhouse gas and to build a green traffic network.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.61.1-61.1
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• 2016

Black hole (BH) mass is a fundamental quantity to understand BH growth, galaxy evolution, and connection between them. Thus, obtaining accurate and precise BH mass estimates over cosmic time is of paramount importance. The rest-frame UV CIV ${\lambda}1549$ broad emission line is commonly used for BH mass estimates in high-redshift AGNs (i.e., $2{\leq}z{\leq}5$) when single-epoch (SE) optical spectra are available. Achieving correct and accurate calibration for CIV-based SE BH mass estimators against the most reliable reverberation-mapping based BH mass estimates is thus practically important and still useful. By performing multi-component spectral decomposition analysis to obtained high-quality HST UV spectra for the updated sample of local reverberation-mapped AGNs including new HST STIS observations, CIV emission line widths and continuum luminosities are consistently measured. Using a Bayesian hierarchical model with MCMC sampling based on Hamiltonian Monte Carlo algorithm (Stan NUTS), we provide the most consistent and accurate calibration of CIV-based BH mass estimators for the three line width characterizations, i.e., full width at half maximum (FWHM), line dispersion (${\sigma}_{line}$), and mean absolute deviation (MAD), in the extended BH mass dynamic range of log $M_{BH}/M_{\odot}=6.5-9.1$.

• Geotechnical Engineering
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• v.7 no.3
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• pp.21-32
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• 1991

The purpose of this paper is to develop a reliability model for slope stability of Earth-rockfill dams which accounts for all uncertainties encountered. The uncertain factors of the design variables include the cohesion, the angle of internal friction, and the porewater Pressure in each zone. More specifically, the model errors in estimating those variables are studied in depth. To reduce the uncertainties due to model errors, updated design variables are obtained using Bayesian Theory. For stability analysis, both the two-dimesional stability analysis and the three-dimensional stability analysis where the end effects and the system reliability concept are considered are used for the reliability calculations. The deterministic safety factor by the three-dimensional analysis is lager than that by the two-dimensional anlysis. However, the probability of failure by the three-dimensional analysis is about 3.5 times larger that by the two-dimensional analysis. It is because the system reliability concept is used in the three-dimensional analysis. The sensitivity analysis shows that the probability of failure is more sensitive to the uncertainty of the cohesion than that of the angle of internal friction.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.147-165
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• 1992

This paper analyzes the macroeconomic effects of elections on the Korean economy and their future ramifications. It measures the shocks to the Korean economy caused by elections by taking the average of sample forecast errors from four major elections held in the 1980s. The seven variables' Bayesian Vector Autoregression Model which includes the Monetary Base, Industrial Production, Consumption, Consumer Price, Exports, and Investment is based on the quarterly time series data starting from 1970 and is updated every quarter before forecasts are made for the next quarter. Because of this updating of coefficients, which reflects in part the rapid structural changes of the Korean economy, this study can capture the shock effect of elections, which is not possible when using election dummies with a fixed coefficient model. In past elections, especially the elections held in the 1980s, $M_2$ did not show any particular movement, but the currency and base money increased during the quarter of the election was held and the increment was partly recalled in the next quarter. The liquidity of interest rates as measured by corporate bond yields fell during the quarter the election and then rose in the following quarter, which is somewhat contrary to the general concern that interest rates will increase during election periods. Manufacturing employment fell in the quarter of the election because workers turned into campaigners. This decline in employment combined with voting holiday produce a sizeable decline in industrial production during the quarter in which elections are held, but production catches up in the next quarter and sometimes more than offsets the disruption caused during the election quarter. The major shocks to price occur in the previous quarter, reflecting the expectational effect and the relaxation of government price control before the election when we simulate the impulse responses of the VAR model, imposing the same shocks that was measured in the past elections for each election to be held in 1992 and assuming that the elections in 1992 will affect the economy in the same manner as in the 1980s elections, 1992 is expected to see a sizeable increase in monetary base due to election and prices increase pressure will be amplified substantially. On the other hand, the consumption increase due to election is expected to be relatively small and the production will not decrease. Despite increased liquidity, a large portion of liquidity in circulation being used as election funds will distort the flow of funds and aggravate the fund shortage causing investments in plant and equipment and construction activities to stagnate. These effects will be greatly amplified if elections for the head of local government are going to be held this year. If mayoral and gubernatorial elections are held after National Assembly elections, their effect on prices and investment will be approximately double what they normally will have been have only congressional and presidential elections been held. Even when mayoral and gubernatorial elections are held at the same time as congressional elections, the elections of local government heads are shown to add substantial effects to the economy for the year. The above results are based on the assumption that this year's elections will shock the economy in the same manner as in past elections. However, elections in consecutive quarters do not give the economy a chance to pause and recuperate from past elections. This year's elections may have greater effects on prices and production than shown in the model's simulations because campaigners' return to industry may be delayed. Therefore, we may not see a rapid recall of money after elections. In view of the surge in the monetary base and price escalation in the periods before and after elections, economic management in 1992 should place its first priority on controlling the monetary aggregate, in particular, stabilizing the growth of the monetary base.