Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model
| Autor: | Chenfei Shao, Zhenzhu Meng, Chongshi Gu, Yating Hu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
displacement
lcsh:Hydraulic engineering Computer science Generalization Geography Planning and Development 0211 other engineering and technologies 020101 civil engineering 02 engineering and technology Aquatic Science Biochemistry Displacement (vector) 0201 civil engineering Data modeling Normal distribution lcsh:Water supply for domestic and industrial purposes iterative self-organizing data analysing lcsh:TC1-978 021105 building & construction Point (geometry) Cluster analysis Water Science and Technology lcsh:TD201-500 random coefficient model Mixture model dam safety Distribution (mathematics) Gaussian mixture model Algorithm |
| Zdroj: | Water, Vol 11, Iss 4, p 714 (2019) Water Volume 11 Issue 4 |
| ISSN: | 2073-4441 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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