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神经网络振动速度预测结果与BP神经网络、萨氏公式的振动速度预测结果进行比较分析。结果表明:BP神经网络的振动速度预测精度显著优于萨氏公式,且经遗传算法优化的BP神经网络振动速度预测精度得到进一步提升。
In order to the vibration effect caused by foundation pit blasting excavation is comprehensively affected by many factors,the traditional empirical formula to predict the vibration speed is difficult to meet the current needs of blasting safety.Therefore,how to optimize blasting parameters and reduce blasting vibration effect is of great significance to ensure the safety of adjacent buildings.Based on the 400 groups of sample data obtained from the on-site blasting monitoring of a foundation pit project,this paper uses genetic algorithm to optimize BP neural network to predict the vibration velocity,and compares the vibration velocity prediction results of GA-BP neural network with those of BP neural network and Saab formula.The results show that the prediction accuracy of vibration velocity of BP neural network is significantly better than that of Saab formula,and the prediction accuracy of vibration velocity of BP neural network optimized by genetic algorithm is further improved.
作者
胡业红
何梦
周参军
丁志宏
蔡长庚
马翔宇
张建经
HU Yehong;HE Meng;ZHOU Canjun;DING Zhihong;CAI Changgeng;MA Xiangyu;ZHANG Jianjing(China Nuclear Huachen Construction Engineering Co.,Ltd.,Fuzhou 350000,China;School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《中国矿业》
2022年第2期72-77,共6页
China Mining Magazine
基金
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)。
关键词
遗传算法
BP神经网络
毫秒延时爆破
回归分析
genetic algorithm
BP neural network
millisecond delay blasting
regression analysis
