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Abstract. This study proposed a new discharge estimation method using a mean velocity formula derived from Chiu's 2D velocity formula of probabilistic entropy concept and the river bed shear stress of channel. In particular, we could calculate the mean velocity, which is hardly measurable in flooding natural rivers, in consideration of several factors reflecting basic hydraulic characteristics such as river bed slope, wetted perimeter, width, and water level that are easily obtainable from rivers. In order to test the proposed method, we used highly reliable flow rate data measured in the field and published in SCI theses, estimated entropy M from the results of the mean velocity formula and, at the same time, calculated the maximum velocity. In particular, we obtained phi(M) expressing the overall equilibrium state of river through regression analysis between the maximum velocity and the mean velocity, and estimated the flow rate from the newly proposed mean velocity formula. The relation between estimated and measured discharge was analyzed through the discrepancy ratio, and the result showed that the estimate value was quite close to the measured data.

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Damage is generally initiated locally and spread to the entire structure. To avoid the destruction of the entire structure, it is crucial to detect and act on damage at an early stage through the real-time monitoring of the entire structure. However, the attachment of the many sensors to obtain sufficient detection resolution could change the structural dynamic characteristics of the structure. To compensate for these shortcomings, research has been conducted on digital image correlation (DIC) as a non-contact method of displacement/strain measurement. In addition, the real-time monitoring of a structure using DIC equipment is relatively straightforward. The final goal of this study is to predict the location of damage using the displacement of the structure surface which can be via DIC. This paper introduces a method for monitoring damage locations using a class activation map (CAM) network. The feasibility of the proposed process using the finite element method for an example considering the experimental situation was confirmed. To generate training data, the finite element method was used to obtain the displacement and strain of a target structure. Herein, sub-structuring approach with data augmentation using Gaussian integration point interpolation were employed to obtain the benign performance of the proposed approach. Thus, the proposed CAM network could classify the presence or absence of damage by considering strain fields. Moreover, the relevant result of the CAM network is a CAM image, which indicates the location of the damage. Finally, this CAM network was applied to a tensile specimen example and show good performance in the classification and detection of damage locations.