尾矿库坝体变形稳定性监测技术研究

发布时间：2018-05-19 00:41

本文选题：尾矿库在线监测 + 稳定性　；参考：《山东理工大学》2015年硕士论文

【摘要】：尾矿坝是一座特殊的矿山工业构筑物,一旦发生溃坝事故,将对企业经济、周边居民的生命财产以及当地生态环境造成不可估量的后果。尾矿坝稳定性不足容易导致溃坝,而尾矿坝发生溃坝、滑坡等事故前期的直观表现是坝体变形。因此,研究尾矿坝的稳定性以及对坝体变形趋势进行监测预测是矿山安全生产中急需解决的首要课题。本文工作主要进行了如下几个方面的深入研究:(1)研究影响尾矿坝稳定性的因素。介绍了尾矿坝组成、作用以及分类特点、安全等别划分及尾矿坝的破坏模式,通过对尾矿物、外部环境、堆积坝以及浸润线等影响尾矿坝稳定性因素进行分析,揭示尾矿坝随着坝体变高、库水位上升,安全系数逐渐减小,稳定性降低。(2)尾矿库坝体变形是一个动态变化的非线性系统,不能用定性模型准确分析和预测,对此提出一种以自适应变异和非线性惯性权重相结合的方法改进粒子群算法,优化支持向量机核参数和惩罚因子,建立基于粒子群-支持向量机的尾矿库坝体变形预测模型。利用实际工程数据验证了该模型的可靠性,结果表明在短期内该模型可以比较准确预测坝体变形位移变化趋势,有利于及时了解尾矿坝的运行状况,从而减少尾矿库风险。(3)研究尾矿库在线安全监测系统,根据相关规范对某尾矿库坝体变形进行监测设计,阐述了该监测系统的构成,通信方式,仪器设备等,并对传感器节点电路进行设计。利用VB开发尾矿库监测预警系统软件,实现对监测信息采集、传输、处理分析、图表显示和预警等功能,并采用混合编程的方法将建立的坝体位移预测模型应用于监测软件系统,为尾矿坝稳定性分析提供有效信息支持,对保障尾矿库安全有重要作用。
[Abstract]:Tailings dam is a special mine industrial structure. Once dam break occurs, it will cause inestimable consequences to enterprise economy, the life and property of the surrounding residents and the local ecological environment. The lack of stability of tailings dam can easily lead to dam break, while dam deformation is the visual manifestation of dam collapse and landslide in the early stage of accidents. Therefore, the study of the stability of tailings dam and the monitoring and prediction of dam deformation trend are the most urgent problems in mine safety production. The main work of this paper is to study the factors that affect the stability of tailings dam. This paper introduces the composition, function and classification characteristics of tailings dam, the classification of safety, and the failure mode of tailings dam. The factors affecting the stability of tailings dam, such as tailings mineral, external environment, accumulation dam and infiltration line, are analyzed. It is revealed that the tailing dam deformation is a dynamic nonlinear system with the dam body increasing, the reservoir water level rising, the safety factor gradually decreasing, and the stability decreasing. The deformation of the tailing dam body can not be accurately analyzed and predicted by the qualitative model. In this paper, an improved particle swarm optimization (PSO) algorithm based on adaptive mutation and nonlinear inertial weight is proposed to optimize kernel parameters and penalty factors of support vector machine (SVM), and a prediction model of tailing dam body deformation based on PSO and SVM is established. The reliability of the model is verified by using the actual engineering data. The results show that the model can accurately predict the deformation and displacement trend of the dam body in the short term and is helpful to understand the operation status of the tailings dam in time. In order to reduce the risk of tailing reservoir, the online safety monitoring system of tailing reservoir is studied, and the deformation monitoring design of a tailing dam body is carried out according to the relevant specifications. The composition, communication mode, instrument and equipment of the monitoring system are expounded. The sensor node circuit is designed. The software of monitoring and early warning system of tailing reservoir is developed by using VB to realize the functions of collecting, transmitting, processing and analyzing of monitoring information, displaying charts and warning, etc. The dam displacement prediction model is applied to the monitoring software system by using the mixed programming method, which provides effective information support for the stability analysis of the tailings dam and plays an important role in ensuring the safety of the tailings dam.
【学位授予单位】：山东理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD926.4

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