[1]关富僳①②,吴发名③,罗志④,等.基于灰色关联分析及GABP模型的岩体爆破块度预测[J].爆破器材,2021,50(04):40-47，53.[doi:10.3969/j.issn.1001-8352.2021.04.008]
　GUAN Fusu,WU Faming,LUO Zhi,et al.Prediction of Rock Blasting Fragmentation Based on Grey Correlation Analysis and GA-BP Model[J].EXPLOSIVE MATERIALS,2021,50(04):40-47，53.[doi:10.3969/j.issn.1001-8352.2021.04.008]
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基于灰色关联分析及GABP模型的岩体爆破块度预测()
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《爆破器材》[ISSN:1001-8352/CN:32-1163/TJ]

卷:
50
期数:
2021年04
页码:
40-47，53
栏目:
爆破技术
出版日期:
2021-07-07

文章信息/Info

Title:
Prediction of Rock Blasting Fragmentation Based on Grey Correlation Analysis and GA-BP Model
文章编号:
5550
作者:
关富僳①②; 吴发名③; 罗志④; 姚强①②; 廖亚斌④; 李洪涛①②
①四川大学水利水电学院（四川成都，610065）
②四川大学水力学与山区河流开发保护国家重点实验室（四川成都，610065）
③中国三峡建设管理有限公司（四川成都，610000）
④中国水利水电第七工程局有限公司（四川成都，610034）
Author(s):
GUAN Fusu①②;  WU Faming③;  LUO Zhi④;  YAO Qiang①②;  LIAO Yabin④;  LI Hongtao①②
①College of Water Resource and Hydropower, Sichuan University (Sichuan Chengdu, 610065)
②State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University (Sichuan Chengdu, 610065)
③China Three Gorges Construction Management Co., Ltd. (Sichuan Chengdu, 610000)
④7th Co., Ltd., Sinohydro Bureau (Sichuan Chengdu, 610034)
关键词:
块度预测; 灰色关联分析; BP网络; 遗传算法; GA-BP模型
Keywords:
prediction of rock fragmentation;  grey correlation analysis;  BP network;  genetic algorithm;  GABP model
分类号:
TV542；TD235
DOI:
10.3969/j.issn.1001-8352.2021.04.008
文献标志码:
A
摘要:
在土石坝筑坝材料的爆破开采过程中，准确预测岩体爆破块度并进行块度控制，可保证土石坝的填筑质量。结合长河坝工程的过渡料现场爆破试验，采用灰色关联分析法分析影响爆破块度的主要因素，以此选取孔距、不均匀系数等分别作为预测模型的输入、输出参数，并采用遗传算法(GA)优化反向传播(BP)神经网络，建立了预测爆破块度的GA-BP模型。该模型的工程应用结果显示，不均匀系数Cu、曲率系数Cc、分形维数D预测值的平均相对误差分别为5.918%、8.862%、2.867%，且预测级配曲线的线形及走向均与实际结果较为接近，表明预测效果良好。对比GA-BP模型与BP网络的预测结果发现，GA-BP模型预测值的平均相对误差更小，表明总体上GA-BP模型优于BP网络。
Abstract:
In the process of blasting mining of rock-fill dam materials, accurately predicting and controlling the blasting fragmentation of rock mass can guarantee the filling quality of rock-fill dam. Combined with the field blasting test of the transition material in Changheba Project, the main influencing factors of blasting fragmentation were analyzed by using grey correlation analysis method, and then hole spacing and non-uniformity coefficient were selected as the input and output parameters of the prediction model respectively. GA-BP model of blasting fragmentation prediction was established by using genetic algorithm (GA) to optimize BP network. Application results of this model show that the average relative errors between the predicted values of the non-uniformity coefficient Cu, curvature coefficient Cc and fractal dimension D?and the actual values are 5.918%, 8.862% and 2.867%, respectively, and the line shape and trend of the predicted grading curve are close to the actual results, indicating that the prediction effect of grading is good. By comparing the predicted results of GA-BP model with those of BP network, the average relative error of GA-BP model is smaller, and the results show that GA-BP model is superior to BP network on the whole.

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相似文献/References:

[1]宋家威①,郇宝乾①,秦涛①,等.基于IGWO-CatBoost模型的岩石爆破块度预测[J].爆破器材,2024,53(02):56.[doi:10.3969/j.issn.1001-8352.2024.02.009]
　SONG Jiawei,HUAN Baoqian,QIN Tao,et al.Prediction of Rock Blasting Fragmentation Based on IGWO-CatBoost Model[J].EXPLOSIVE MATERIALS,2024,53(04):56.[doi:10.3969/j.issn.1001-8352.2024.02.009]

备注/Memo

备注/Memo:
收稿日期：2020-12-10
基金项目：国家重点研发计划项目（2018YFC1508501, 2018YFC1505402, 2018YFC0406800）;国家自然科学基金项目（51809188）
第一作者：关富僳（1996-），男，硕士研究生，主要从事工程爆破研究。E-mail: guanfs1996@163.com
通信作者：李洪涛（1979-），男，教授，主要从事水利水电施工和工程爆破研究。E-mail: htl@scu.edu.cn
更新日期/Last Update: 2021-07-08