Abstract
Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monitoring points are overlooked, which leads to the over-fitting problem and the limitations in the generalization of model. A novel model combines Gaussian mixture model and Iterative self-organizing data analysing (ISODATA-GMM) clustering and the random coefficient method is proposed in this article, which takes the temporal-spatial correlation among the monitoring points into account. By taking the temporal-spatial correlation among the monitoring points into account and building models for all the points simultaneously, the random coefficient model improves the generalization ability of the model through reducing the number of free model variables. Since the random coefficient model supposed the data follows normal distributions, we use an ISODATA-GMM clustering algorithm to classify the measuring points into several groups according to its temporal and spatial characteristics, so that each group follows one distribution. Our model has the advantage of having a stronger generalization ability.
Highlights
Dam safety monitoring aims to understand the actual running status of the dam, so as to provide sufficient information to ensure the safety of the concrete dam [1]
After we classified the measuring points into five classes using clustering analysis based on the Iterative Self-Organizing Data Analysis (ISODATA)-Gaussian Mixture Model (GMM) method, we developed a random coefficient model for each class
Using the ISODATA-GMM method and random coefficient model, we fitted the displacement data from 16 June 2013 to 15 June 2015 to develop the prediction model
Summary
Dam safety monitoring aims to understand the actual running status of the dam, so as to provide sufficient information to ensure the safety of the concrete dam [1]. Researchers developed statistical models, in which the displacement δ at each monitoring point can be approximated by:. The random coefficient model can model the data of several monitoring points synchronously, and make the explanatory variables’ coefficients of each measuring point satisfy asymptotic normal distributions [12,13]. One is based on similarity or dissimilarity distances such as hierarchical cluster analysis [14] and K-means algorithm [15]. We clustered the data using the Gaussian mixture model (GMM), which assumes a multivariate Gaussian distribution for each component.
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