Damage to a huge dam can cause great loss of human life and property, but disasters and their consequences can be minimized by implementing effective dam safety monitoring strategies. However, establishing a permanent monitoring system on a huge dam is costly. Additionally, for reasons of national security, many dams and information about them may not be able to be accessed by researchers. Accordingly, continuously monitoring the structural health of a dam by measurement may be difficult. This study presents a way to continuously monitor the health of a dam using vibration signals that are measured not on the dam but close to it. The Sayano-Shushenskaya Dam in Russia is used to demonstrate the idea. Intensive ambient vibration measurements were firstly made once to determine the natural frequencies of the dam. Then the natural frequencies of the dam under varying environmental effects are obtained from the spectra of the seismic records obtained at Cheryomushki seismic station, which is located 4.4 km northeast of the dam. To account for the effects of varying environmental conditions on the natural frequencies, an autoencoder in the form of an unsupervised learning neural network, was employed. The autoencoder was trained using the natural frequencies without using any environmental factors to learn the intrinsic behavior of the dam under varying environmental conditions. The errors between input data to the trained autoencoder and the regenerated data from the autoencoder can be used to determine whether the dam is under normal conditions. A finite element model of the dam was constructed to simulate changes of natural frequencies due to cracks in the dam structure. The results demonstrate that the proposed method can feasibly monitor the structural health of the dam.
Structure failure often occurs in the joint. This failure can adversely affect the comfort level of the structure. Knowing the behavior of interior beam-column joint structure resulting from the load is important, as it can help to predict the strength of the structure interior beam-column joint and comfort of the structure being worked on. One way to find out and predict the strength and comfort of the structure interior beam-column joint as a result of the load received is the experimental test and simulation. The simulation VecTor2 used to predict the shear force, crack, and displacement of the reinforced concrete column when applied the shear force. This simulation considered the effect of the bond stress-slip effect of behavior reinforced concrete. Bonds stress-slip gives a great influence on the strength and hysteretic response of the reinforced concrete beam-column joint. That is why this study considers the influence of the bond stress-slip on a reinforced concrete column. All the result of simulation VecTor2 using bond stress-slip effect would be compared with the result of the experimental test to see the behavior and accuracy of the simulation test.
In this paper, the nonlinear analysis of the motion structures is studied by using the vector form intrinsic finite element (VFIFE, V-5) method. The main object of this research is to develop an internal hinge of two ends of the plane frame element. In this study, the hinge function of frame element is used to compute the nonlinear dynamic responses of the motion structures. A fictitious reversed rigid body motion can be used to separate the rigid body motions and the pure deformations of the frame element. It is not requires any iteration or any parameters for the VFIFE method during computation process. Four examples illustrate the accuracy of the proposed procedures in computing large motions of a flying flexible structure.
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