黄金科学技术 ›› 2024, Vol. 32 ›› Issue (4): 620-630.doi: 10.11872/j.issn.1005-2518.2024.04.022
Zhenyang LI1(),Baogang ZHANG1,Xin XIONG2,Chengye YANG2,Yuqi BAI1
摘要:
爆破振动的质点峰值速度(Peak Particle Velocity,PPV)是评估爆破开采设计参数合理性的重要指标。为实现一种PPV有效预测模型,借助粒子群优化算法(Particle Swarm Optimization,PSO),结合优化极端梯度提升树(Extreme gradient boosting,XGBoost),构建了一种参数自优化的PSO-XGBoost预测模型。以LK露天铜钴矿为研究对象,选取最大单段炸药量、总炸药量、测量的爆心距、炮孔平均进深尺度和高程差共5个影响因素指标作为研究参数,通过现场收集187次爆破作业实测数据,进一步开展PPV的PSO-XGBoost预测研究,并与传统XGBoost模型、SSA-XGBoost优化模型及萨道夫斯基经验公式的PPV回归预测进行对比分析,最后利用Shapley Additive Explanatory(SHAP)方法开展影响PPV预测结果的敏感性因素分析。结果表明:PSO-XGBoost预测模型的预测评价指标计算结果最优(R2=0.921,RMSE=0.0752,MAE=0.0717,MBE=0.0683),其对PPV的预测结果明显优于传统XGBoost模型、SSA-XGBoost混合优化模型及萨道夫斯基经验公式,同时,敏感性分析得到总炸药用量是影响PPV预测结果的重要参数。进一步说明PSO-XGBoost预测模型可处理多因素的非线性特征,利用PSO-XGBoost预测模型能够更好地结合非线性、离散数据,建立一种可靠、简单有效的PPV预测模型。研究结果可为露天矿山爆破振动快速预测提供参考。
中图分类号:
Deng Hongwei, Luo Liang,2023.PPV prediction model based on random forest optimized by SMA algorithm[J].Gold Science and Technology,31(4):624-634. | |
Dong Donglin, Zhang Longqiang, Zhang Enyu,et al,2023.A rapid identification model of mine water inrush based on PSO-XGBoost[J].Coal Science and Technology,51(7):72-82. | |
Fan Yong, Pei Yong, Yang Guangdong,et al,2022.Prediction of blasting vibration velocity peak based on an improved PSO-BP neural network[J].Journal of Vibration and Shock,41(16):194-200. | |
Gou Qianqian, Zhao Mingsheng, Chi En’an,et al,2018.Prediction and application of evaluation factors in blasting vibration based on PCA-BP neural network[J].Mining Research and Development,38(12):97-102. | |
Guo Xiaoqiang,2021.Study on the determination of the limit distance of boundary borehole based on blasting vibration[J].Mining Research and Development,41(6):26-30. | |
He Li, Liu Yihe, Li Linna,et al,2022.Prediction of mine Blasting vibration velocity of mines based on particle swarm -least square support vector machine model[J].Metal Mines,51(7):145-150. | |
Hong Z X, Tao M, Liu L L,et al,2023.An intelligent approach for predicting overbreak in underground blasting operation based on an optimized XGBoost model[J].Engineering Applications of Artificial Intelligence,126(2):1-16. | |
Hu Xiaobing, Chen Zhiyuan, Wei Geping,et al,2020.Blasting vibration prediction system based on BP neural network[J].Mining Research and Development,40(9):154-158. | |
Khandelwal M, Armaghani D J, Faradonbeh R S,et al,2017.Classification and regression tree technique in estimating peak particle velocity caused by blasting[J].Engineering with Computers,33:45-53. | |
Li D T, Yan J L, Zhang L,2012.Prediction of blast-induced ground vibration using support vector machine by tunnel excavation[J].Applied Mechanics and Materials,170/171/172/173:1414-1418. | |
Ma C L, Wang W H, Wang S X,et al,2023.Prediction of shear strength of RC slender beams based on interpretable machine learning[J].Structures,57:105171. | |
Sadovsky M A,1952.Mechanical action of blast waves on data of experimental studies[J].Physics of Explosion,12(1):70-110. | |
Shi Chunyu, Zhang Zhihong, Zhou Jie,et al,2023.Study on blasting vibration transmission characteristics of rock mass of structural planes with different angles [J].Mining Research and Development,43(5):184-189. | |
Shirani Faradonbeh R, Jahed Armaghani D, Abd Majid M Z,et al,2016.Prediction of ground vibration due to quarry blasting based on gene expression programming:A new model for peak particle velocity prediction[J].International Journal of Environmental Science and Technology,13(6):1453-1464. | |
Trabi B, Bleibinhaus F,2023.Blast vibration prediction[J].Geophysical Prospecting,71(7):1312-1324. | |
Tyagi P, Sharma A, Semwal R,et al,2023.XGBoost odor prediction model:Finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm[J].Journal of Biomolecular Structure and Dynamics,12(2):1-15. | |
Wang Ziyi, Wu Guiyi, Luo Chang,et al,2023.Study on vibration response and stability of steep slope under multiple blasting vibration[J].Blasting,40(3):158-169. | |
Xu Xiangdong, Lu Yu,2023.Study on empirical formula of blasting vibration propagation reflecting the influence of height difference[J].Automation and Instrumentation,(6):63-66. | |
Xuan G Q, Wu X Y,2023.Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction[J].Measurement,218:113106. | |
Yu Z, Li C Q, Zhou J,2023.Tunnel boring machine performance prediction using supervised learning method and swarm intelligence algorithm[J].Mathematics,11(20):4237. | |
Zeng Xiaohui, Zhang Xuemin, Dai Bin,et al,2023.Prediction of tunnel blasting vibration velocity considering influence of number of free surfaces and resistance line[J].China Work Safety Science and Technology,19(6):83-89. | |
Zhang Xiliang,2020.Ensemble Learning Model and Engineering Application of Environmental Effect Prediction of Rock Mass Blasting[D].Hefei:University of Science and Technology of China. | |
Zhou J, Qiu Y G, Khandelwal M,et al,2021.Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations[J].International Journal of Rock Mechanics and Mining Sciences,145(1):104856. | |
Zou Ping, Wang Liang, Dai Yong,et al,2023.Establishment and application of blasting vibration prediction system based on SSA-XGBoost [J].Blasting,40(3):199-205. | |
邓红卫,罗亮,2023.基于SMA算法优化随机森林的PPV预测模型[J].黄金科学技术,31(4):624-634. | |
董东林,张陇强,张恩雨,等,2023.基于PSO-XGBoost的矿井突水水源快速判识模型[J].煤炭科学技术,51(7):72-82. | |
范勇,裴勇,杨广栋,等,2022.基于改进 PSO-BP 神经网络的爆破振动速度峰值预测[J].振动与冲击,41(16):194-200. | |
苟倩倩,赵明生,池恩安,等,2018.基于PCA-BP神经网络在爆破振动评价要素中的预测及应用[J].矿业研究与开发,38(12):97-102. | |
郭晓强,2021.基于爆破振动的极限边孔排距的确定方法研究[J].矿业研究与开发,41(6):26-30. | |
何理,刘易和,李琳娜,等,2022.基于粒子群—最小二乘支持向量机模型的矿山爆破振动速度预测[J].金属矿山,51(7):145-150. | |
胡晓冰,陈志远,魏格平,等,2020.基于BP神经网络的爆破振动预测系统[J].矿业研究与开发,40(9):154-158. | |
史春宇,张志鸿,周杰,等,2023.不同角度结构面岩体的爆破振动传播特性研究[J].矿业研究与开发,43(5):184-189. | |
王子一,吴桂义,罗畅,等,2023.多次爆破振动下陡边坡振动响应及稳定性研究[J].爆破,40(3):158-169. | |
徐向东,陆瑜,2023.反映高差影响的爆破振动传播经验公式研究[J].自动化与仪器仪表,(6):63-66. | |
曾晓辉,张学民,戴斌,等,2023.考虑自由面数量和抵抗线影响的隧道爆破振速预测[J].中国安全生产科学技术,19(6):83-89. | |
张西良,2020.岩体爆破环境效应预测的集成学习模型及工程应用[D].合肥:中国科学技术大学. | |
邹平,王亮,戴勇,等,2023.基于SSA-XGBoost的爆破振动预测系统的构建与应用[J].爆破,40(3):199-205. |
[1] | 李地元, 杨博, 刘子达, 刘永平, 赵君杰. 基于集成树算法的岩石黏聚力和内摩擦角预测方法[J]. 黄金科学技术, 2024, 32(5): 847-859. |
[2] | 李炎, 王建国, 魏生云, 李国璋, 胡建, 王志男. 西藏德新铅多金属矿床地球物理与地球化学综合找矿研究[J]. 黄金科学技术, 2024, 32(3): 400-415. |
[3] | 李祥龙, 余林, 黄原明, 陈浩, 赵艳伟. 基于VMD-HHT的井下预裂爆破振动效应分析[J]. 黄金科学技术, 2024, 32(3): 501-510. |
[4] | 姜志宏, 陈澳. 融合全监督学习的半监督矿石粒度预测算法[J]. 黄金科学技术, 2024, 32(3): 539-547. |
[5] | 代树红, 张战军, 柳凯, 郑昊, 孙清林. 基于PEMD-MPE算法的露天矿爆破振动信号降噪方法[J]. 黄金科学技术, 2024, 32(1): 82-90. |
[6] | 凡兴禹, 王雪林. 基于改进XGBoost算法的深部巷道松动圈智能预测研究[J]. 黄金科学技术, 2024, 32(1): 109-122. |
[7] | 海龙, 冯丽鑫, 谭世林, 吕勇博. 根土复合体加固露天矿山排土场边坡研究[J]. 黄金科学技术, 2023, 31(6): 911-918. |
[8] | 许方颖, 邹艳红, 易卓炜, 杨福强, 毛先成. 基于非均衡数据的ADASYN-CatBoost测井岩性智能识别——以胶西北招贤金矿床为例[J]. 黄金科学技术, 2023, 31(5): 721-735. |
[9] | 邓红卫, 罗亮. 基于SMA算法优化随机森林的PPV预测模型[J]. 黄金科学技术, 2023, 31(4): 624-634. |
[10] | 王鹏飞,毕林,王李管. 露天矿无人驾驶矿卡速度规划研究[J]. 黄金科学技术, 2022, 30(3): 460-469. |
[11] | 吴钦正,李润然,李桂林,李金平,尹延天,徐帅. 基于JKSimBlast软件的露天矿爆破毫秒延期时间优化[J]. 黄金科学技术, 2021, 29(6): 854-862. |
[12] | 胡建华,郭萌萌,周坦,张涛. 基于改进迁移学习算法的岩体质量评价模型[J]. 黄金科学技术, 2021, 29(6): 826-833. |
[13] | 张美道,饶运章,徐文峰,王文涛. 全尾砂膏体充填配比优化正交试验[J]. 黄金科学技术, 2021, 29(5): 740-748. |
[14] | 景岳,王少锋,鲁金涛. 矿岩开挖松动区厚度预测及非爆机械化开采判据[J]. 黄金科学技术, 2021, 29(4): 525-534. |
[15] | 刘奇,岑佑华,刘东锐,罗卫兵,徐喜. 基于静态沉降试验的全尾砂浓密技术参数预测[J]. 黄金科学技术, 2021, 29(2): 266-274. |
|