Abstract

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.

Highlights

  • Tailings disposal has always been an important concern worldwide [1–3], with the goal of protecting the environment and people from the hazards associated with tailings storage

  • II, III and IV seismic waves) to characterize the waveform; (3) Depth from the surface to represent the overlying effective pressure; (4) Actual acceleration; (5) Holding time; (6) Earth pressure; whether the drainage characteristics and (7) Dynamic shear stress ratio characterized by (8) pore pressure as the influence characteristics of tailings liquefaction flow threshold under earthquake are suitable as the influence characteristics of liquefaction occurrence

  • This paper focuses on the Particle Swarm Optimization (PSO)-support vector machine (SVM) algorithm to predict whether liquefaction will

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Summary

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

Jiaxu Jin 1,2 , Shihao Yuan 1 , Hongzhi Cui 3, * , Xiaochun Xiao 1 and Baoxin Jia 1. College of Civil and Transportation Engineering, Hohai University, Nanjing 210003, China

Introduction
Test Equipment
Test Material
Test Model Box
Sensor
Test Loading Scheme
Particle Swarm Optimization (PSO)
Support Vector Machine (SVM)
Algorithm Design
Factor Analysis of Influence Parameters of Tailings Sand Liquefaction
Normalization of the Influencing Factors
Construction thethe
PSO-SVM
Analysis
12. Comparison
Findings
Conclusions
Full Text
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