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[1]王发刚,邹平,王忠康,等.基于GA-BP神经网络边坡稳定性预测的方法及应用*[J].中国安全生产科学技术,2024,20(6):161-167.[doi:10.11731/j.issn.1673-193x.2024.06.022]
　WANG Fagang,ZOU Ping,WANG Zhongkang,et al.Method and application of slope stability prediction based on GA-BP neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(6):161-167.[doi:10.11731/j.issn.1673-193x.2024.06.022]
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基于GA-BP神经网络边坡稳定性预测的方法及应用*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年6期
页码:
161-167
栏目:
职业安全卫生管理与技术
出版日期:
2024-06-30

文章信息/Info

Title:
Method and application of slope stability prediction based on GA-BP neural network
文章编号:
1673-193X(2024)-06-0161-07
作者:
王发刚; 邹平; 王忠康; 戴勇; 肖祖荣; 刘正宇
(1.低品位难处理黄金资源综合利用国家重点实验室，福建 上杭 364204；
2.紫金矿业集团股份有限公司，福建 上杭 364200；
3.紫金(长沙)工程技术有限公司，湖南 长沙 410017)
Author(s):
WANG Fagang;  ZOU Ping;  WANG Zhongkang;  DAI Yong;  XIAO Zurong;  LIU Zhengyu
(1.State Key Laboratory of Comprehensive Utilization of Low-Grade Refractory Gold Ores,Shanghang Fujian 364204,China;
2.Zijin Mining Group Co.,Ltd.,Shanghang Fujian 364200,China;
3.Zijin (Changsha) Engineering Technology Co.,Ltd.,Changsha Hunan 410017,China)
关键词:
遗传算法; BP神经网络; 可视化分析; 边坡稳定性
Keywords:
genetic algorithm;  BP neural network;  visualization analysis;  slope stability
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2024.06.022
文献标志码:
A
摘要:
为更有效预测边坡安全系数，以边坡的6个主要特征（重度γ、黏聚力c、内摩擦角φ、边坡角α、边坡高度H和孔隙水压力ru）参数为研究基础，构建基于遗传算法优化BP神经网络的边坡稳定性预测模型。首先，收集205组边坡案例建立样本数据集，采用分布小提琴图和皮尔逊相关性分析系数检验矩阵进行特征参数分布特征与相关性的可视化分析；然后，采用构建的预测模型进行训练和测试；最后，对测试结果进行验证。研究结果表明：各特征参数的小提琴图分布相类似，特征参数之间相关性不显著，样本数据集较为合理；GA-BP与BP神经网络预测结果整体上均接近真实值，而采用遗传算法优化后的模型在预测方面具有更好的准确度和稳度。研究结果可为边坡稳定性状态判断提供一定参考。
Abstract:
In order to more effectively predict the safety factor of slopes,a slope stability prediction model based on genetic algorithm optimized BP neural network is constructed based on the six main characteristics of slopes (weight γ,cohesion c,internal friction angle φ,slope angle α,slope height H,and pore water pressure ru).Firstly,205 sets of slope cases were collected to establish a sample dataset.The distribution violin plot and Pearson correlation analysis coefficient test matrix were used to visualize the distribution characteristics and correlation of characteristic parameters;Then,use the constructed prediction model for training and testing;Finally,validate the test results.The research results indicate that the violin plot distribution of each feature parameter is similar,and the correlation between feature parameters is not significant.The sample dataset is relatively reasonable;The prediction results of GA-BP and BP neural networks are generally close to the true values,while the model optimized by genetic algorithm has better accuracy and stability in prediction.The research results can provide certain reference for the judgment of slope stability status.

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备注/Memo

备注/Memo:
收稿日期： 2024-01-21
* 基金项目： 国家重点研发计划项目（2022YFC2903904）
作者简介： 王发刚，硕士，助理工程师，主要研究方向为矿山岩土工程灾害防控。
通信作者： 邹平，博士，正高级工程师，主要研究方向为矿山开采技术与安全。
更新日期/Last Update: 2024-06-25