Investigation of monotonic and cyclic behavior of sand using a bounding surface plasticity model

Zahmatkesh, A.; Noorzad, Reza

doi:10.1007/s12517-018-3389-6

Cited by 2 publications

(1 citation statement)

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“…However, the randomness, complexity and uncertainty of a soil mass make it difficult to adopt appropriate empirical equations for regression analysis [14][15][16][17][18]. Therefore, many experts and scholars try to develop simpler and more practical scientific analysis models to predict the possibility of liquefaction and establish methods to distinguish the liquefaction of sandy soil [19]:…”

Section: Introductionmentioning

confidence: 99%

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

et al. 2022

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The liquefaction of tailings sand caused by seismic loads is a major problem in ensuring the safety of tailings ponds. Liquefaction may cause uncontrolled fluidized failure of the dam body, causing considerable damage to the lives, property and environment of people downstream. In this paper, a prototype tailings sand is used as the material to consider the main factors affecting liquefaction (i.e., dynamic load, soil quality, burial and static conditions). By embedding acceleration, pore pressure and earth pressure sensors in the rigid design of the self-designed rigid model box, different types of seismic waves of different ground motion amplitudes (PGA) were induced in a shaking table test of tailings sand liquefaction. The seismic intensity, waveform (class II, III and IV seismic waves) and active earth pressure of the PGA characterizing dynamic factors were obtained, and the static factors were characterized. The dynamic shear stress ratio, the peak acceleration of the earthquake, the pore pressure of the drainage factor and the buried depth (overlying effective pressure) characterize the soil conditions. SPSS software was used to analyze the factor dimension reduction, and the most suitable factors for factor analysis were obtained. Particle Swarm Optimization (PSO) was used to optimize the parameters, and the improved PSO-SVM algorithm was compared with the existing genetic algorithm (GA) and grid node search (GS). The algorithm used in this paper is fast and has a relatively high accuracy rate of 92.7%. The established threshold model method is of great significance to predict the liquefaction of tailings sand soil under the action of ground motions and to carry out safety managemenin advance, which can provide a certain reference for the project.

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