基于贝叶斯决策的尾矿坝干滩长度预警研究

doi:10.16265/j.cnki.issn1003-3033.2022.S1.0650

中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (S1): 120-126.doi: 10.16265/j.cnki.issn1003-3033.2022.S1.0650

基于贝叶斯决策的尾矿坝干滩长度预警研究

刘迪¹^,²(), 卢才武¹^,², 连民杰¹^,³, 顾清华¹^,²^,^**(), 景莹²^,⁴

¹ 西安建筑科技大学资源工程学院, 陕西西安 710005
² 西安市智慧工业感知计算与决策重点实验室, 陕西西安 710005
³ 中钢集团矿业开发有限公司, 北京100080
⁴ 西安建筑科技大学管理学院, 陕西西安 710005

收稿日期:2022-01-20 修回日期:2022-04-10 出版日期:2022-06-30 发布日期:2022-12-30
通讯作者: 顾清华
作者简介:
刘迪 (1987—),女,陕西咸阳人,博士,工程师,主要从事尾矿坝稳定性方面的研究。E-mail: safety_ld2010@126.com。
卢才武教授
连民杰教授
基金资助:
国家自然科学基金资助(52074205)

Dry beach length pre-warning of tailings dam based on Bayesian decision

LIU Di¹^,²(), LU Caiwu¹^,², LIAN Minjie¹^,³, GU Qinghua¹^,²^,^**(), JING Ying²^,⁴

¹ School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
² Xi 'an Key Laboratory of Perceptive Computing and Decision for Intelligent Industry, Xi'an Shaanxi 710055, China
³ China Sinosteel Mining Co., Ltd., Beijing 100080, China
⁴ School of Management, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China

Received:2022-01-20 Revised:2022-04-10 Online:2022-06-30 Published:2022-12-30
Contact: GU Qinghua

摘要/Abstract

摘要：

为合理确定尾矿坝干滩长度的监测预警阈值,首先,分析干滩长度和渗透系数的变化特性,结合尾矿坝稳定性分析与贝叶斯决策理论,将干滩长度数据离散化,采用随机变量表示干滩长度的不确定性;然后,依据相关规程将坝体安全系数的范围离散化,建立安全系数与干滩长度间关联的贝叶斯决策模型,并依据最小风险分析原则划分贝叶斯决策分类,再通过最大后验概率确定干滩长度的预警阈值;最后,将该预警模型应用于陕西某尾矿库的干滩长度预警。研究表明:当干滩长度小于170 m时,该尾矿坝存在溃坝风险,通过排渗可使干滩长度增加至175 m以满足坝体稳定性要求。贝叶斯决策预警数据与企业监测数据吻合,预警阈值能客观反应尾矿坝的稳定性状况。

关键词: 贝叶斯决策, 尾矿坝, 干滩长度, 预警阈值, 稳定性分析

Abstract:

In order to reasonably determine early warning threshold of dry beach length of tailings dam, firstly, change characteristics of dry beach length and permeability coefficient were analyzed, length data were discretized based on stability analysis of tailings dam and Bayesian decision theory, and its uncertainty was expressed by random variable. Secondly, safety factor range of dam bodies was discretized according to relevant regulations, a Bayesian decision model of correlation between safety factor and dry beach length was established, and Bayesian decision classification was conducted according to principle of minimum risk analysis before warning threshold of dry beach length was determined through maximum posterior probability. Finally, the early warning method was applied to a tailings pond in Shaanxi province. The results show that when the dry beach length is less than 170 m, tailings dam has the risk of dam failure, but the stability requirement can be met by increasing its length to 175 m through drainage. The early warning data of Bayesian decision is consistent with monitoring data of enterprises, and the warning threshold can objectively reflect stability state of tailings dam.

Key words: Bayesian decision, tailings dam, dry beach length, early warning threshold, stability analysis

刘迪, 卢才武, 连民杰, 顾清华, 景莹. 基于贝叶斯决策的尾矿坝干滩长度预警研究[J]. 中国安全科学学报, 2022, 32(S1): 120-126.

LIU Di, LU Caiwu, LIAN Minjie, GU Qinghua, JING Ying. Dry beach length pre-warning of tailings dam based on Bayesian decision[J]. China Safety Science Journal, 2022, 32(S1): 120-126.

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岩土名称	天然容重/ (kN·m^-3)	饱和容重/ (kN·m^-3)	黏聚力/ kPa	内摩擦角/ (°)	饱和黏聚力/ kPa	饱和内摩擦角/ (°)	饱和含水量/ %	饱和渗透系数/ 10^-6(m·s^-1)
①尾粉砂	19	19.5	9.5	33	8	28	29.8	1.0~24
①_-1尾粉土	20	20.5	13	25	12	22	33.5	0.75~3.00
②尾粉土	20	20.5	13	25	12	22	33.5	0.75~3.0
③尾粉质土	20.6	20.6	10	15	10	15	45.3	0.02~0.6
④_-1坝体填土	18.5	19.5	30	28	28	25	9.6	1.0~36
④黄土状土	20.1	20.1	18	25	18	25	39.3	3.0~6.0
⑤千枚岩	23	23	2 000	60	2 000	60	0.3	0.1~0.3

区间长度/ m	10	15	20	25
最大后验概率	2.69	3.89	4.82	6.08
对应取值区间	[150,160]	[155,170]	[150, 170]	[145,170]
决策风险值	0.002 0	0.001 4	0.001 5	0.001

基于贝叶斯决策的尾矿坝干滩长度预警研究

Dry beach length pre-warning of tailings dam based on Bayesian decision

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