• Journal of the Korea Concrete Institute
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• v.24 no.6
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• pp.651-658
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• 2012

This paper concentrates on the evaluation of microcracks in thermal damaged concrete on the basis of the nonlinear ultrasonic modulation technique. Since concrete structure exposed to high temperature accompanies the development of microcracks due to the physical and chemical changes from temperature and exposed time, the adoption of nonlinear approach is required. Instead of using the conventional ultrasonic nondestructive methods which have the limitation in evaluating excessive microcracks, accordingly, a nonlinear ultrasonic modulation method which shows better sensitivity in quantifying microcracks is introduced. Upon the analysis for the modulation of ultrasonic wave and low frequency impact to measure the nonlinearity parameter, which can be used as an indicator of thermal damage, the verification processes for the introduced technique are followed: SEM investigation and permeable pore space test are performed to characterize thermally induced microcracks in concrete, and ultrasonic pulse velocity tests are performed to confirm the outstanding sensitivity of nonlinear ultrasonic modulation technique. In advance, compressive strength of thermal damaged concrete is measured to represent the effect of microcracks on performance degradation. Correlation studies between experimental data and measured data show that nonlinear ultrasonic modulation technique can effectively be used to quantify thermally induced microcracks, and to estimate the compressive strength of thermally damaged concrete.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1061-1069
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• 2021

Recently, the magnitude and frequency of extreme heavy rains and localized heavy rains have increased due to abnormal climate, which caused increased flood damage in river basin. As a result, the nonlinearity of the hydrological system of rivers or basins is increasing, and there is a limitation in that the lead time is insufficient to predict the water level using the existing physical-based hydrological model. This study predicted the water level at Ulsan (Taehwagyo) with a lead time of 0, 1, 2, 3, 6, 12 hours by applying deep learning techniques based on Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and evaluated the prediction accuracy. As a result, DNN model using the sliding window concept showed the highest accuracy with a correlation coefficient of 0.97 and RMSE of 0.82 m. If deep learning-based water level prediction using a DNN model is performed in the future, high prediction accuracy and sufficient lead time can be secured than water level prediction using existing physical-based hydrological models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.2
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• pp.145-152
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• 2010

This study evaluated the fatigue degradation in a SUS316L specimen using the nonlinear ultrasonic method. The nonlinearity of the ultrasonic wave was estimated by a relative nonlinear parameter defined as the ratio of the amplitudes for the fundamental wave to the second harmonic wave. In the experiment, a measurement system with contact transducers was constructed; reliable measurements were assured by keeping measurement conditions consistent and reducing extra harmonics generated in the measurement system. Two types of SUS316L specimen were used in experiments; a rotating bar fatigue specimen and a tensile fatigue specimen. The fatigue condition used was high cycle fatigue. The former specimen had a cylindrical shape and was used to observe the change in the nonlinear parameter after fatigue accumulation in a specimen. The latter was a plate-shaped specimen and was used to confirm the change in the nonlinear parameter at the position where the fatigue stress was concentrated. The measured nonlinear parameter showed a strong correlation to the damage level in both fatigue tests.