Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model
The neural network algorithm is a small sample machine learning method built on the statistical learning theory and the lowest structural risk principle. Classical neural network algorithms mainly aim at solving two-classification problems, making it infeasible for multiclassification problems encou...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/3603853 |
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| _version_ | 1832546019464708096 |
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| author | Yu Wang Jiachen Wang |
| author_facet | Yu Wang Jiachen Wang |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | The neural network algorithm is a small sample machine learning method built on the statistical learning theory and the lowest structural risk principle. Classical neural network algorithms mainly aim at solving two-classification problems, making it infeasible for multiclassification problems encountered in engineering practice. According to the main factors affecting sand liquefaction, a sand liquefaction discriminant model based on a clustering-binary tree multiclass neural network algorithm is established using the class distance idea in cluster analysis. The model can establish the nonlinear relationship between sand liquefaction and various influencing factors by learning limited samples. The research results show that the hierarchical structure based on the clustering-binary tree neural network algorithm is reasonable, and the sand liquefaction level can be categorized accurately. |
| format | Article |
| id | doaj-art-d4d3ab87637b4e2a8430ab088fd3a31d |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d4d3ab87637b4e2a8430ab088fd3a31d2025-02-03T07:24:09ZengWileyAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/3603853Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm ModelYu Wang0Jiachen Wang1School of Civil EngineeringSchool of Civil EngineeringThe neural network algorithm is a small sample machine learning method built on the statistical learning theory and the lowest structural risk principle. Classical neural network algorithms mainly aim at solving two-classification problems, making it infeasible for multiclassification problems encountered in engineering practice. According to the main factors affecting sand liquefaction, a sand liquefaction discriminant model based on a clustering-binary tree multiclass neural network algorithm is established using the class distance idea in cluster analysis. The model can establish the nonlinear relationship between sand liquefaction and various influencing factors by learning limited samples. The research results show that the hierarchical structure based on the clustering-binary tree neural network algorithm is reasonable, and the sand liquefaction level can be categorized accurately.http://dx.doi.org/10.1155/2021/3603853 |
| spellingShingle | Yu Wang Jiachen Wang Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model Advances in Civil Engineering |
| title | Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model |
| title_full | Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model |
| title_fullStr | Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model |
| title_full_unstemmed | Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model |
| title_short | Sandy Soil Liquefaction Prediction Based on Clustering-Binary Tree Neural Network Algorithm Model |
| title_sort | sandy soil liquefaction prediction based on clustering binary tree neural network algorithm model |
| url | http://dx.doi.org/10.1155/2021/3603853 |
| work_keys_str_mv | AT yuwang sandysoilliquefactionpredictionbasedonclusteringbinarytreeneuralnetworkalgorithmmodel AT jiachenwang sandysoilliquefactionpredictionbasedonclusteringbinarytreeneuralnetworkalgorithmmodel |