Continuous structural health monitoring of the Sayano-Shushenskaya Dam using off-site seismic station data accounting for environmental effects
Journal
Measurement Science and Technology
Journal Volume
31
Journal Issue
1
Date Issued
2020
Author(s)
Hsu, T.-Y.
Valentino, A.
Liseikin, A.
Krechetov, D.
Chen, C.-C.
Lin, T.-K.
Wang, R.-Z.
Chang, K.-C.
Seleznev, V.
Abstract
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. ? 2019 IOP Publishing Ltd.
Subjects
autoencoder; environmental effect; natural frequency; off-site monitoring; Sayano-Shushenskaya Dam
Other Subjects
Environmental impact; Learning systems; National security; Natural frequencies; Seismology; Structural health monitoring; Auto encoders; Continuous structural health monitoring; Environmental conditions; Environmental factors; Intrinsic behavior; Learning neural networks; Off sites; Structural health; Dams
Type
journal article