• 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.

• Smart Structures and Systems
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• v.7 no.1
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• pp.15-25
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• 2011

The mechanical properties obtained from mechanical tests, such as tensile, buckling, impact and fatigue tests, are largely applied to several materials and are used today for preliminary studies for the investigation of a desired element in a structure and prediction of its behavior in use. This contribution focus on two widely used different tests: tensile and fatigue tests. Small PZT (Lead Titanate Zirconate) patches are bonded on the surface of test samples for impedance-based health monitoring purposes. Together with these two tests, the electromechanical impedance technique was performed by using aluminum test samples similar to those used in the aeronautical industry. The results obtained both from tensile and fatigue tests were compared with the impedance signatures. Finally, statistical meta-models were built to investigate the possibility of determining the state of the structure from the impedance signatures.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Health Policy and Management
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• v.30 no.4
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• pp.433-437
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• 2020

Cancer is a disease which has the huge burden in worldwide, and cancer is the number one cause of death in Korea. At this point, the new framework for cancer monitoring index is required for regional cancer monitoring. Especially, cancer survivors are the important target which is rapidly increasing recently, also cancer survivor's quality of care should be considered in the cancer monitoring index framework. To develop the Multidimensional Cancer Monitoring Index considering cancer survivor's quality of care, we took into account cancer continuum which including prevention, detection, diagnosis, treatment, survivorship, assessment of quality of care and monitoring cancer patient, and end-of life care for stage. For target, components of health care delivery system such as patient, family, provider, payer, and policy maker are included. Also, Donabedian model which is a framework for examining health services and evaluating quality of health care such as structure, process, and outcome is applied to contents. This new cancer monitoring framework which includes multidimensional components could help to develop regional cancer monitoring index, and to make national cancer management and prevention policy in the future.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Smart Structures and Systems
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• v.12 no.2
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• pp.181-207
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• 2013

In this paper, a statistical reference-free real-time damage detection methodology is proposed for detecting joint and member damage of truss bridge structures. For the statistical damage sensitive index (DSI), wavelet packet decomposition (WPD) in conjunction with the log likelihood ratio was suggested. A sensitivity test for selecting a wavelet packet that is most sensitive to damage level was conducted and determination of the level of decomposition was also described. Advantages of the proposed method for applications to real-time health monitoring systems were demonstrated by using the log likelihood ratios instead of likelihood ratios. A laboratory truss bridge structure instrumented with accelerometers and a shaker was used for experimental verification tests of the proposed methodology. The statistical reference-free real-time damage detection algorithm was successfully implemented and verified by detecting three damage types frequently observed in truss bridge structures - such as loss of bolts, loosening of bolts at multiple locations, sectional loss of members - without reference signals from pristine structure. The DSI based on WPD and the log likelihood ratio showed consistent and reliable results under different damage scenarios.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.751-760
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• 2017

In recent years, multifractal-based analysis methods have been widely applied in engineering. Among these methods, multifractal detrended cross-correlation analysis (MFDXA), a branch of fractal analysis, has been successfully applied in the fields of finance and biomedicine. For its great potential in reflecting the subtle characteristic among signals, a structural health monitoring (SHM) system based on MFDXA is proposed. In this system, damage assessment is conducted by exploiting the concept of multifractal theory to quantify the complexity of the vibration signal measured from a structure. According to the proposed algorithm, the damage condition is first distinguished by multifractal detrended fluctuation analysis. Subsequently, the relationship between the q-order, q-order detrended covariance, and length of segment is further explored. The dissimilarity between damaged and undamaged cases is visualized on contour diagrams, and the damage location can thus be detected using signals measured from different floors. Moreover, a damage index is proposed to efficiently enhance the SHM process. A seven-story benchmark structure, located at the National Center for Research on Earthquake Engineering (NCREE), was employed for an experimental verification to demonstrate the performance of the proposed SHM algorithm. According to the results, the damage condition and orientation could be correctly identified using the MFDXA algorithm and the proposed damage index. Since only the ambient vibration signal is required along with a set of initial reference measurements, the proposed SHM system can provide a lower cost, efficient, and reliable monitoring process.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.148-151
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• 2009

In these days it is important to secure the life and stability of the structure such as aircrafts, automobiles and building. So the structural health monitoring is needed. In conventional lamb wave techniques, damage is identified by comparing the measured data (baseline signals) and the current data. But this method can lead to high false signal in the intact condition of the structure due to environmental conditions of the structure. As a solution to resolve it, the structural health monitoring method which doesn't use baseline signals is necessary. Damaged structure has unusual elastic wave. This paper proposed a PC(pitch-catch) method which doesn't use baseline signal. New baseline signals can get from detection signal. Damage signals based on new baseline signals. This paper made an image includes damage information by applying damage-signals to beamformming.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Advances in concrete construction
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• v.11 no.5
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• pp.375-386
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• 2021

In this paper, a study of stability and health of a newly-built railway tunnel is presented. The field test was implemented to monitor the secondary lining due to the significant cracking behaviors influenced the stability and health of the tunnel structure. Surface strain gauges were installed for monitoring the status of crack openings, and the monitoring outputs demonstrated that the cracks were still in the developing stage. Additionally, adjacent tunnel and poor condition of surrounding rock were identified as the causes of the lining cracking by systematically characterizing the crack spatial distribution, tunnel site and surrounding rock conditions. Reconstruction of partial lining and reconstruction of the whole secondary lining were designed as the maintenance projects for different cracking regions based on the construction feasibility. For assessing the health conditions of the reinforced lining, embedded strain gauges were set up to continuously measure the strain and the internal force of the reconstructed structures. For the partially reconstructed lining, the outputs show the maximum tensile elongation is 0.018 mm during 227 days, which means the structure has no obvious deformation after maintenance. The one-year monitoring of full-section was implemented in the other two completely reconstructed cross-sections by embedded strain gauge. The outputs show the reconstructed secondary lining has undertaken the pressure of surrounding rock with the time passing. According to the calculated compressive and tensile safety factors, the completely reconstructed lining has been in reliable and safe condition during the past year after reinforcement. It can conclude that the aforementioned maintenance projects can effectively ensure the stability and health of this tunnel.