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Jing Y.-L.,Changan University | Wu Y.-Q.,Shanghai JiaoTong University | Lin D.-J.,China Coal Xian Design and Engineering Co. | Hu Z.-P.,Changan University | And 2 more authors.
Yantu Lixue/Rock and Soil Mechanics | Year: 2011

Loess collapsibility test and compaction test have been carried out. The concept of compaction rate is suggusted. The classification and regression trees (CART) algorithm is improved; and is used for analyzing the correlative (data) mining. The correlative mining has been performed to undisturbed loess collapsibility and index of compaction test. The results of correlative mining indicates that coefficient of collapsibility is closely correlated to the compaction rate, but significantly in negative correlation with the collapsibility coefficients. In the compaction process, the closer the soil water content is to the optimal water content, the stronger this correlation is. Finally, this paper presents a viewpoint that undisturbed loess collapsibility is correlated with the engineering property of the compaction loess, and undisturbed loess collapsibility can be evaluated with deformation property of disturbed loess samples. This is a new method of evaluating loess collapsibility. Source

Jing Y.-L.,Changan University | Wu Y.-Q.,Shanghai JiaoTong University | Lin D.-J.,China Coal Xian Design and Engineering Co. | Li X.-G.,Changan University | Zhang Z.-Q.,Changan University
Yantu Lixue/Rock and Soil Mechanics | Year: 2010

The data mining techniques are used to predict loess collapsibility in geotechnical engineering; and the mining model is constructed by using the least squares support vector machines. Using principal component analysis method, data of model is preprocessed to remove the correlation among the indicators and to eliminate the impact of multi-index redundant information on the mining model, and the model inverse analysis is administered by introducing particle swarm optimization algorithm to determine the optimal parameters. The forecast mining for the actual project data shows that loess resistivity and shear wave velocity is closely related to the soil indicators such as the soil structural properties, water content, density etc, they can be more comprehensively reflect the factors of impact of loess collapsibility. Using loess resistivity, shear wave velocity and soil depth as predicting variables of the model can quantitative predict the loess collapsibility; the proposed model is effective. Source

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