Abstract
Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.
Highlights
With the rapid development of Internet of Things (IoT) technologies, various types of IoT devices can be deployed in large dam engineering to measure the different physical quantities and sense their changes in various regions of the structure, such as deformation, stress, pressure, etc. [1,2,3,4]
Different the gridded partition results of the dam safety monitoring network based on the mechanical models
In order to describe the dynamics of the physical quantities for the dam safety monitoring through the network partition in a real-time way, a network partitioning algorithm based on the spatial-temporal features of sensor nodes (NPA) was proposed
Summary
With the rapid development of Internet of Things (IoT) technologies, various types of IoT devices (e.g., sensor nodes) can be deployed in large dam engineering to measure the different physical quantities and sense their changes in various regions of the structure, such as deformation, stress, pressure, etc. [1,2,3,4]. In the dam safety monitoring systems, the massive monitoring data are generated from the deployed sensor nodes. The monitoring network is usually divided into multiple regions according to the design specifications, simulation data, and engineering experience. Taking an arch dam as an example, based on the dam sections and elevation, the monitoring network can be divided into an arch dam as an example, based on the dam sections and elevation, the monitoring network can be multiple grids adopting horizontal and vertical partitions, respectively. Different the gridded partition results of the dam safety monitoring network based on the mechanical models
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