| 基于GA-BP神经网络的毫秒延时爆破振动速度预测研究 |
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| 引用本文： | 胡业红,何 梦,周参军,丁志宏,蔡长庚,马翔宇,张建经. 基于GA-BP神经网络的毫秒延时爆破振动速度预测研究[J]. 中国矿业, 2022, 31(2): 72-77 |
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| 作者姓名： | 胡业红 何 梦 周参军 丁志宏 蔡长庚 马翔宇 张建经 |
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| 作者单位： | 中核华辰建筑工程有限公司,中核华辰建筑工程有限公司,中核华辰建筑工程有限公司,中核华辰建筑工程有限公司,中核华辰建筑工程有限公司,中核华辰建筑工程有限公司,西南交通大学土木工程学院 |
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| 基金项目： | 中核集团科研创新项目资助（编号：CNEC11010233000-FWHT-20-0002）;国家重点研发计划项目资助（编号：2017YFC0504901）;四川省科技计划项目资助（编号：2015SZ0068） |
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| 摘 要： | 由于基坑爆破开挖作用而产生的振动效应受多种因素综合影响,传统的经验公式预测振动速度难以满足目前爆破安全的需求.因此,如何优化爆破参数,减小爆破振动效应,对保证临近建筑的安全具有重要意义.基于某基坑工程现场爆破监测所得的400组样本数据,本文采用遗传算法(GA)优化BP神经网络,对振动速度进行预测,将GA-BP神经网络振...
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| 关 键 词： | 遗传算法 BP神经网络 毫秒延时爆破 回归分析 |
| 收稿时间： | 2020-11-13 |
| 修稿时间： | 2022-01-28 |
Study on vibration velocity prediction of millisecond delay blasting based on GA-BP neural network |
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| HU Yehong,HE Meng,ZHOU Canjun,DING Zhihong,CAI Changgeng,MA Xiangyu and ZHANG Jianjing. Study on vibration velocity prediction of millisecond delay blasting based on GA-BP neural network[J]. CHINA MINING MAGAZINE, 2022, 31(2): 72-77 |
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| Authors: | HU Yehong HE Meng ZHOU Canjun DING Zhihong CAI Changgeng MA Xiangyu ZHANG Jianjing |
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| Affiliation: | China Nuclear Huachen Construction CO,LTD,Fujian Fuzhou,China Nuclear Huachen Construction CO,LTD,Fujian Fuzhou,China Nuclear Huachen Construction CO,LTD,Fujian Fuzhou,China Nuclear Huachen Construction CO,LTD,Fujian Fuzhou,China Nuclear Huachen Construction CO,LTD,Fujian Fuzhou,China Nuclear Huachen Construction CO,LTD,Fujian Fuzhou,School of Architecture and Construction,Southwest Jiaotong University,Sichuan Chengdu |
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| Abstract: | The vibration effect of foundation pit blasting excavation is influenced by many factors, and the traditional empirical formulas can not meet the safety demand of blasting. Therefore, how to optimize blasting parameters and reduce blasting vibration effect is of great significance to ensure the safety of adjacent existing buildings. Based on 400 sets of sample data obtained from blasting monitoring in a foundation pit project, the BP neural network was optimized by genetic algorithm (GA) to predict the vibration velocity, and the predicted results of GA-BP neural network were compared with those of BP neural network and Sadov"s formula. The results show that the prediction accuracy of BP neural network is significantly better than that of Sadov"s formula, and the prediction accuracy of BP neural network optimized by genetic algorithm is further improved. |
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| Keywords: | genetic algorithm BP neural network vibration speed millisecond delay blasting regression analysis |
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