• Smart Structures and Systems
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• 제20권2호
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2001년도 가을 학술발표회 논문집
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• pp.101-108
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• 2001

In the previous study, an improved QRD (QR Decomposition)-ILS(Iterative Least-Squares) method is proposed to estimate the structural parameters at the element level using response data alone without using any information of excitation measurements for the assessment of local damages and deterioration in complex and large structural systems. But for a complex and large structural system, where response measurement at every dynamic degree of freedom(DDOF) is not possible, the absence of some observation points of responses and its effect on the proposed SI method must be studied In the paper, a QRD-ILS technique that utilizes the known intact stiffness information estimated based on the visual inspection, field measurements and/or NDT tests is proposed to identify local damages of fracture critical members using measured responses only at limited DDOFs. A numerical example is used to illustrate the application of this technique. The results indicate that the proposed SI technique is very simple but efficient, since no input information are required with only limited observations.

• Smart Structures and Systems
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• 제30권6호
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• pp.649-659
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• 2022

Structural Health Monitoring (SHM) researchers have identified Augmented Reality (AR) as a new technology that can assist inspections. Post-seismic structural inspections are conducted to evaluate the safety level of the damaged structures. Quantification of nearby structural changes over short-term and long-term periods can provide building inspectors with information to improve their safety. This paper proposes a Time Machine Measure (TMM) application based on an Augmented Reality (AR) Head-Mounted-Device (HMD) platform. The primary function of TMM is to restore the saved meshes of a past environment and overlay them onto the real environment so that inspectors can intuitively measure dynamic structural deformation and other environmental movements. The proposed TMM application was verified by demo experiments simulating a real inspection environment.

• 전산구조공학
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• 제21권4호
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• pp.35-45
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• 2008

• Structural Engineering and Mechanics
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• 제58권6호
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• pp.1077-1085
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• 2016

In the last few years, many structural monitoring studies have been performed using different techniques to measure structures of different scales such as buildings, dams or bridges. One of the mostly used tools are GPS instruments, which have been utilized in various combinations with accelerometers and some other conventional sensors. In the current study, observation series were recorded for 8 hours with GPS receivers (NovAtel) and Inclination Measurement Sensors mounted on a television tower in Istanbul, Turkey. Each series of observations collected from two different sensors were transformed into a single coordinate system (Local Topocentric Coordinates System). The positional changes of the tower were calculated from the GPS and the inclination data. These changes were plotted in two dimensions (2D) on the same graphic. Thus, the possibility of comparison and analysis were found using the data from both the GPS and the Inclinometer complement each other, in the real test area. The positional changes of the tower were modeled for further examination. As a result, the movement of the tower within an area of $1{\times}1cm^2$ was observed. Based on the results, it can be concluded that inclinometers can be used for monitoring the structural behavior of the tower.

• 국제초고층학회논문집
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• 제4권3호
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• pp.191-198
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• 2015

It is important to accurately predict structural responses to external excitations such as typhoons and earthquakes when designing structures for serviceability. One of the key procedures to predict reliable vibration responses is to evaluate accurate structural dynamic properties using finite element (FE) models, which properly represent the realistic behavior of buildings. In the case of historic masonry buildings, structural damage could also be caused by ambient vibrations or impacts. Therefore, the preservation plans of historic buildings for low-level vibrations or impacts should be provided by analyzing structural damages within serviceability levels. For this purpose, it is required to provide FE model construction and response analysis methods verified with field measurement data. In this research, long-term field measurement was performed for a cathedral and its dynamic properties were evaluated using measured data. Then, the model was calibrated based on the measured dynamic properties and an overall construction method for the masonry cathedral was proposed. Using the measured accelerations, the vibrations of the belfry were analyzed using the calibrated FE model and finally, the FE model for the cathedral was verified by comparing the measured accelerations with the modeled results.

• Smart Structures and Systems
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• 제30권3호
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Smart Structures and Systems
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• 제31권3호
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• pp.301-309
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• 2023

Tilt is a key indicator of structural safety. Real-time monitoring of tilt responses helps to evaluate structural condition, enable cost-effective maintenance, and enhance lifetime resilience. This paper presents a prototype wireless sensing system for structural tilt measurement. Long range (LoRa) technology is adopted by the sensing system to offer long-range wireless communication with low power consumption. The sensor integrates a gyroscope and an accelerometer as the sensing module. Although tilt can be estimated from the gyroscope or the accelerometer measurements, these estimates suffer from either drift issue or high noise. To address this challenging issue and obtain more reliable tilt results, two sensor fusion algorithms, the complementary filter and the Kalman filter, are investigated to fully exploit the advantages of both gyroscope and accelerometer measurements. Numerical simulation is carried out to validate and compare the sensor fusion algorithms. Laboratory experiment is conducted on a simply supported beam under moving vehicle load to further investigate the performance of the proposed wireless tilt sensing system.

• 한국수학교육학회지시리즈A:수학교육
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• 제56권4호
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• pp.407-434
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• 2017

The purpose of this study is to develop and verify the TPACK measurement tool for middle and high school mathematics teachers in the Korean context. Also, by clarifying the relationship between subordinate factors of Mathematics teachers' TPACK, an attempt was made to provide suggestions on the designs and directions for the in-service and pre-service teacher education and the programs for improving mathematics teachers' TPACK in the future. In order to achieve this goal, TPACK factors of mathematics teachers were extracted by reviewing literature on PCK, MKT, and TPACK. Then, content validity, basic statistical survey, reliability verification, exploratory factor analysis, confirmatory factor analysis, and structural equation model verification were conducted sequentially. At first, preliminary analysis was carried out on 79 mathematics teachers, and 76 items excluding the items with extreme value and reliability were included in the basic statistical analysis. And secondly, an exploratory factor analysis was conducted on 376 mathematics teachers, and this instrument consisted of 7 subordinate factors(CK, PK, TK, PCK, TCK, TPK, TPACK) and 61 items. Also by conducting confirmatory factor analysis and structural equation model test with 254 mathematics teachers, the measurement tool was confirmed the validity and reliability through statistically significant analysis. Then, the importance of integrated knowledge was confirmed by looking at the relationship between the TPACK factors of in-service mathematics teachers. The integrated knowledge(PCK, TCK, TPK) has played a crucial role in the formation of TPACK rather than the knowledge of CK, PK, and TK alone. Finally, the validity of TCK was confirmed through the structural equation modeling of TPACK. TCK not only directly affected TPACK, but also indirectly through TPK. According to these affirmative results, this measurement tool is claimed to be suitable for measuring the factors of Mathematics teachers' TPACK, and also the structural equation model can be regarded as a suitable model for analyzing the structural relationship of mathematics teachers' TPACK.

• Communications for Statistical Applications and Methods
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• 제22권4호
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.