Calculation method of damage effects of underground engineering objectives based on data mining technology

ZHANG Lei; WU Hao; ZHAO Qiang; WANG Xing; REN Xinjian; WANG Jimin; KONG Defeng

doi:10.11883/bzycj-2020-0114

Volume 41 Issue 3

Mar. 2021

Turn off MathJax

Article Contents

Article Navigation > Explosion And Shock Waves > 2021 > 41(3): 031101

ZHANG Lei, WU Hao, ZHAO Qiang, WANG Xing, REN Xinjian, WANG Jimin, KONG Defeng. Calculation method of damage effects of underground engineering objectives based on data mining technology[J]. Explosion And Shock Waves, 2021, 41(3): 031101. doi: 10.11883/bzycj-2020-0114

Citation:

ZHANG Lei, WU Hao, ZHAO Qiang, WANG Xing, REN Xinjian, WANG Jimin, KONG Defeng. Calculation method of damage effects of underground engineering objectives based on data mining technology[J]. Explosion And Shock Waves, 2021, 41(3): 031101. doi: 10.11883/bzycj-2020-0114

Citation:

ZHANG Lei, WU Hao, ZHAO Qiang, WANG Xing, REN Xinjian, WANG Jimin, KONG Defeng. Calculation method of damage effects of underground engineering objectives based on data mining technology[J]. Explosion And Shock Waves, 2021, 41(3): 031101. doi: 10.11883/bzycj-2020-0114

PDF( 1103 KB)

Calculation method of damage effects of underground engineering objectives based on data mining technology

doi: 10.11883/bzycj-2020-0114

1.
Institute of Defense Engineering, Academy of Militory Science of PLA, Luoyang 471023, Henan, China
2.
Academy of Military Sciences, People's Liberation Army, Beijing 100071, China
3.
College of Computer and Information, Hehai University, Nanjing 211110, Jiangsu, China

Received Date: 2020-04-17
Rev Recd Date: 2020-11-26

Available Online: 2021-03-05

Publish Date: 2021-03-10

Abstract

Abstract

Aiming at low calculation accuracy of damage effect caused by less data, uneven, discontinuity and narrow distribution of damage experimental data, data mining technology is introduced to calculate damage effect. The database manages damage metadata and the data cleaning technology is used to identify and eliminate dead points’ data in order to control the data quality in database. An algorithm evaluation method is established to select the optimal empirical algorithm. The dimensionality reduction of high-dimensional damage data is achieved through feature selection and the main control parameters are chosen to train neural network model and k-nearest neighbor search. The “three-stage” damage effects calculation model based on data fusion has been established. The model can be used to calculate weapon damage effect based on experimental data, the empirical algorithm and the BP neural network model. The software has been developed to complete the damage calculation, and the results shows that the proposed method can meet the needs of practical application.
- data mining,
- damage effects,
- data quality analysis,
- feature selection,
- k-nearest neighbor search,
- neural network

FullText(HTML)

References(20)

References

[1]	任辉启, 穆朝民, 刘瑞朝, 等. 精确制导武器侵彻效应与工程防护[M]. 北京: 科学出版社, 2016: 54−58.
[2]	KANTARDZIC M. Data mining: concepts, models, methods, and algorithms [M]. 2nd ed. Hoboken: Wiley Publishing, Inc, 2011: 135−137.
[3]	HE Z, WU Q, WEN L J, et al. A process mining approach to improve emergency rescue processes of fatal gas explosion accidents in Chinese coal mines [J]. Safety Science, 2019, 111: 154–166. DOI: 10.1016/j.ssci.2018.07.006.
[4]	RYAN S, THALER S. Artificial neural networks for characterizing Whipple shield performance [J]. Procedia Engineering, 2013, 58: 31–38. DOI: 10.1016/j.proeng.2013.05.006.
[5]	RYAN S, THALER S, KANDANAARACHCHI S. Machine learning methods for predicting the outcome of hypervelocity impact events [J]. Expert Systems with Applications, 2016, 45: 23–39. DOI: 10.1016/j.eswa.2015.09.038.
[6]	李建光, 李永池, 王玉岚. 人工神经网络在弹体侵彻混凝土深度中的应用 [J]. 中国工程科学, 2007, 9(8): 77–81. DOI: 10.3969/j.issn.1009-1742.2007.08.016. LI J G, LI Y C, WANG Y L. Penetration depth of projectiles into concrete using artificial neural network [J]. Engineering Sciences, 2007, 9(8): 77–81. DOI: 10.3969/j.issn.1009-1742.2007.08.016.
[7]	金胜兵, 刘军, 张磊, 等. 数据挖掘技术在混凝土侵彻深度分析中的应用[J]. 解放军理工大学学报(自然科学版) [2020-09-21]. http://kns.cnki.net/kcms/detail/32.1430.N.20170602.1014.002.html. DOI: 10.12018/j.issn.1009-3443.20170223003/2017.06.02. JIN S B, LIU J, ZHANG L, et al. The application of data mining technology in the analysis of the projectile penetration depth in concrete[J]. Journal of PLA University of Science and Technology (Natural Science Edition) [2020-09-21]. http://kns.cnki.net/kcms/detail/32.1430.N.20170602.1014.002.html. DOI: 10.12018/j.issn.1009-3443.20170223003/2017.06.02.
[8]	杨秀敏, 邓国强. 常规钻地武器破坏效应的研究现状和发展 [J]. 后勤工程学院学报, 2016, 32(5): 1–9. DOI: 10.3969/j.issn.1672-7843.2016.05.001. YANG X M, DENG G Q. The research status and development of damage effect of conventional earth penetration weapon [J]. Journal of Logistical Engineering University, 2016, 32(5): 1–9. DOI: 10.3969/j.issn.1672-7843.2016.05.001.
[9]	张国星, 强洪夫, 陈福振, 等. 钻地弹侵彻地下工事问题的研究与发展 [J]. 飞航导弹, 2018(6): 34–38. DOI: 10.16338/j.issn.1009-1319.20170372. ZHAGN G X, QIANG H F, CHEN F Z, et al. Research and development of the penetration of ground-penetrating projectiles into underground engineering [J]. Aerodynamic Missile Journal, 2018(6): 34–38. DOI: 10.16338/j.issn.1009-1319.20170372.
[10]	梁国栋. 钻地弹攻击地下目标的效能评估[D]. 南京: 南京理工大学, 2007: 18−24.
[11]	吴越. 钻地弹对典型地堡侵爆复合毁伤效能影响要素研究[D]. 南京: 南京理工大学, 2014: 45−67.
[12]	潘丽娜. 神经网络及其组合模型在时间序列预测中的研究与应用[D]. 兰州: 兰州大学, 2018: 32−56.
[13]	黄丽. 基于云平台的预测分析算法的研究与实现[D]. 北京: 北京邮电大学, 2016: 1−32, 24−35.
[14]	FLETCHER L, KATKOVNIK V, STEFFENS F E, et al. Optimizing the number of hidden nodes of a feedforward artificial neural network [C]//Proceedings of 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence. Anchorage: IEEE, 1998. DOI: 10.1109/IJCNN.1998.686018.
[15]	GWALTNEY R C. Missile generation and protection in light-water-cooled power reactor plants: ORNL NSIC-22 [R]. Oak Ridge: Oak Ridge National Laboratory, 1968: 1−23.
[16]	YOUNG C W. The development of empirical equation for predicting depth of an earth penetrating projectile: SC-DR-67-60 [R]. Albuquerque: Sandia National Laboratories, 1967: 1−11.
[17]	NDRC. Effects of impact and explosion: division 2 [R]. Washington: National Defence Research Committee, 1946: 1−7.
[18]	FORRESTAL M J, ALTMAN B S, CARGILE J D, et al. An empirical equation for penetration depth of ogive-nose projectiles into concrete targets [J]. International Journal of Impact Engineering, 1994, 15(4): 395–405. DOI: 10.1016/0734-743X(94)80024-4.
[19]	HAN J W, KAMBER M, PEI J. Data mining: concepts and techniques [M]. 3rd ed. Amsterdam: Elsevier Inc, 2012.
[20]	BECKMANN N, KRIEGEL H P, SCHNEIDER R, et al. The R*-tree: an efficient and robust access method for points and rectangles [J]. Acm Sigmod Record, 1990, 19(2): 322–331. DOI: 10.1145/93605.98741.