This study presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in significant impedance variations, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results of real structures, such as civil, mechanical, and aerospace structures. In order to minimize the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited. In this study, cross-correlation coefficient (CC) after an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, is utilized. To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLAB® is developed and incorporated with the current hardware system. Validation of the proposed technique is carried out on a lab-sized steel truss bridge member under a temperature varying environment. It has been found that the CC values have shown significant fluctuations due to the temperature variation, even after applying the EFS method. Therefore, an outlier analysis providing the optimal decision limits under the inevitable variations has been carried out for more systematic damage detection. It has been found that the threshold level shall be properly selected considering the daily temperature range and the minimum target damage level for detection. It has been demonstrated that the proposed strategy combining the EFS and the outlier analysis can be effectively used in the automated continuous SHM of critical structural members under temperature variations.

The impedance-based structural health monitoring (SHM) method has come to the forefront in the SHM community due to its practical potential for real applications. In the impedance-based SHM method, the selection of optimal frequency ranges plays an important role in improving the sensitivity of damage detection, since an improper frequency range can lead to erroneous damage detection results and provide false positive damage alarms. To tackle this issue, this paper proposes an innovative technique for autonomous selection of damage-sensitive frequency ranges using artificial neural networks (ANNs). First, the impedance signals are obtained in a wide frequency band, and the signals are split into multiple sub-ranges of this wide band. Then, the predefined damage index is evaluated for each sub-range by comparing impedance signals between the intact and the concurrent cases. Here, the cross correlation coefficients (CCs) are used as the predefined damage index. The ANN is constructed and trained using all CC values at multiple frequency ranges as multi-inputs and the real damage severity as the single output for various preselected damage scenarios, so that subsequent damage estimations may be carried out by selecting the governing frequency ranges autonomously. The performance of the proposed approach has been examined via a series of experimental studies to detect loose bolts and cracks induced on real steel bridge and building structures. It is found that the proposed approach autonomously determines the damage-sensitive frequency ranges and can be used for effective evaluation of damage severity in a wide variety of damage cases in real structures.

Propacetamol was found to be a major suspected drug of pharmacologically associated hypotension in Korea. Older and male patients taking medications in combination with propacetamol/paracetamol should undergo monitoring of their blood pressure. Copyright © 2017 John Wiley & Sons, Ltd.

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This paper presents a non-destructive evaluation (NDE) technique for detecting damages on a jointed steel plate on the basis of the time of flight and wavelet coefficient, obtained from wavelet transforms of Lamb wave signals. Probabilistic neural networks (PNNs) and support vector machines (SVMs), which are tools for pattern classification problems, were applied to the damage estimation. Two kinds of damages were artificially introduced by loosening bolts located in the path of the Lamb waves and those out of the path. The damage cases were used for the establishment of the optimal decision boundaries which divide each damage class's region from the intact class. In this study, the applicability of the PNNs and SVMs was investigated for the damages in and out of the Lamb wave path. It has been found that the present methods are very efficient in detecting the damages simulated by loose bolts on the jointed steel plate.

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>> In this study, a shaking table test was performed for the evaluation of water extinguishing facilities. Water extinguishing facilities, such as a general pipe, a seismic pipe (Loof type) and a pump, were used in the experiment. This captured the dynamic characteristics of water extinguishing systems by earthquake records at El-Centro with a 50%, 70%, 100%, 120% level. As a result, seismic type facilities have excellent seismic performance compared to general facilities. By using the acceleration response spectrum, not only is the performance evaluation of water extinguishing facilities able to be determined, but also the deformation of facilities in low earthquake levels can be known. This proposed approach can determine the seismic performance evaluation of water extinguishing facilities and verify seismic performance criteria.