摘要
为了更好的预测爆破振动速度,运用遗传算法(GA)对BP神经网络的权值与阈值进行优化,构建GA-BP神经网络预测模型。结合华润小径湾实际爆破工程监测数据,确定以最大单段药量、爆心距以及测点至爆心的高程差作为输入参数,对爆破振动速度进行预测,并与BP神经网络模型的预测结果进行对比。结果表明:GA-BP神经网络模型预测结果的平均相对误差为5.80%,明显小于BP神经网络模型预测的平均误差14.19%。相比之下,GA-BP神经网络模型的预测精度更高,稳定性更好,可为多因素影响下类似工程爆破振动速度预测提供借鉴经验。
The genetic algorithm(GA)is used to optimize the weight and threshold of BP neural network,and the GA-BP neural network prediction model is constructed to predict the blasting vibration velocity precisely.In the actual blasting engineering of China Resources Xiaojingwan,monitoring data,including the maximum single-dose amount,the blasting distance and the elevation difference from the measuring point to the blasting heart,is taken as input parameters.The blasting vibration velocity predicted by the GA-BP neural network model is compared with the one soluted by the BP neural network.The results show that the average relative error of the GA-BP neural network model is 5.80%,which is significantly smaller than that(14.19%)predicted by the BP neural network model.As a result,the GA-BP neural network model presents higher prediction accuracy and better stability,which can provide reference experience for similar engineering blasting vibration velocity prediction under the influence of multiple factors.
作者
郭钦鹏
杨仕教
朱忠华
相志斌
张紫晗
胡光球
GUO Qin-peng;YANG Shi-jiao;ZHU Zhong-hua;XIANG Zhi-bin;ZHANG Zi-han;HU Guang-qiu(School of Resourse Environment and Safety Engineering,University of South China,Hengyang 421000,China;Guangdong Xiyuan Blasting Technology Co Ltd,Huizhou 516000,China)
出处
《爆破》
CSCD
北大核心
2020年第3期148-152,共5页
Blasting
基金
国家自然科学基金项目(50974076)
广东锡源爆破科技股份有限公司项目资助。
关键词
爆破振速
GA-BP神经网络
权值阈值
预测模型
blasting vibration velocity
GA-BP neural network
weight threshold
predictive model