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A platform for research: civil engineering, architecture and urbanism
Data Divison Method of Sand Liquefaction Samples Based on Self-Organizing Maps
Abstract To some extent, artificial neural networks (ANNs) had been successfully applied to the classification of sand liquefaction. In majority of which, data division was carried out on an arbitrary basis. However, the way on how data were divided may have a significant effect on model performance. In this paper, data division and its impact on ANN model were investigated through evaluating the possibility of sand liquefaction on condition of shallow foundations. And a new data division method was investigated: data division using self-organizing maps (SOMs). Results indicated that a statistical property of data in training, testing, and validation sets needs to be taken into account for ensuring optimal model performance. Obviously, the SOM clustering method was suitable for data division.
Data Divison Method of Sand Liquefaction Samples Based on Self-Organizing Maps
Abstract To some extent, artificial neural networks (ANNs) had been successfully applied to the classification of sand liquefaction. In majority of which, data division was carried out on an arbitrary basis. However, the way on how data were divided may have a significant effect on model performance. In this paper, data division and its impact on ANN model were investigated through evaluating the possibility of sand liquefaction on condition of shallow foundations. And a new data division method was investigated: data division using self-organizing maps (SOMs). Results indicated that a statistical property of data in training, testing, and validation sets needs to be taken into account for ensuring optimal model performance. Obviously, the SOM clustering method was suitable for data division.
Data Divison Method of Sand Liquefaction Samples Based on Self-Organizing Maps
Liu, Si-Si (author) / Zhao, Ming-Hua (author)
Advances in Environmental Geotechnics ; 443-448
2010-01-01
6 pages
Article/Chapter (Book)
Electronic Resource
English
Innovation and Professional Development (IPD) Divison
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