Continuous structural health monitoring of the Sayano-Shushenskaya Dam using off-site seismic station data accounting for environmental effects

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.

An overview of technological advancements in the application of structural health monitoring on glass elements

“…displacement monitoring [39]), identifying structural condition (e.g. crack recognition [40]), informing preemptive, accounting for environmental effects [41], and recovery decisions by extracting patterns from data collected via various sources and media [42]. Intelligent diagnosis based on DL algorithms has received increasing attention and has achieved remarkable results in areas such as image classification, object detection, natural language processing, and image segmentation [43].…”

Monitoring of the hydraulic units operation of the Sayano‐Shushenskaya hydroelectric power plant using remote seismic observations

“…It should be noted that the above results can be obtained not only from the direct analysis of the data recorded in the dam body, but also through the analysis of data at remote seismic stations. 29,[37][38][39][40] In our case, these are seismic stations located approximately 4 and 11 km from the dam (Figure 10).…”

Deformation health diagnosis of RCC dams considering construction interfaces based on monitoring data and numerical simulation

“…Roller compacted concrete (RCC) dams, which have advantages such as low cement dosage, simple construction process, downstream areas [4]. Therefore, long-term structural health monitoring (SHM) of RCC dams for deformation health diagnosis and analysis is an important means to ensure the stable operation and structural performance of dams [5,6]. Owing to the construction technology of intermittent thin-layer paving and rolling, RCC dam bodies contain numerous construction interfaces [7][8][9].…”