Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge

QIN Shuai; LIU Hao; CHEN Li; ZHANG Lei

doi:10.11883/bzycj-2023-0287

Volume 44 Issue 3

Mar. 2024

Turn off MathJax

Article Contents

Article Navigation > Explosion And Shock Waves > 2024 > 44(3): 031406

QIN Shuai, LIU Hao, CHEN Li, ZHANG Lei. Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge[J]. Explosion And Shock Waves, 2024, 44(3): 031406. doi: 10.11883/bzycj-2023-0287

Citation:

QIN Shuai, LIU Hao, CHEN Li, ZHANG Lei. Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge[J]. Explosion And Shock Waves, 2024, 44(3): 031406. doi: 10.11883/bzycj-2023-0287

Citation:

PDF( 1146 KB)

Outlier detection algorithms for penetration depth data of concrete targets combined with prior knowledge

doi: 10.11883/bzycj-2023-0287

QIN Shuai^{1, 2
,},
LIU Hao^{1, 3},
CHEN Li¹,
ZHANG Lei^{1
,
,}

1.
Institute of Defense Engineering, AMS, Luoyang 471000, Henan, China
2.
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, Heilongjiang, China
3.
College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China

Received Date: 2023-08-14
Rev Recd Date: 2023-11-23

Available Online: 2023-11-24

Publish Date: 2024-03-14

Abstract

Abstract

Data quality is the basis for the validity and accuracy of data-driven models, and there may be a large number of anomalies in the raw concrete targets penetration depth data. Therefore, to ensure the accuracy of the subsequent data-driven model, it is necessary to eliminate the outlier of the raw data. Compared with the traditional anomaly detection method, the anomaly detection method based on neural network models is more suitable for complex multi-dimensional and unevenly distributed concrete target penetration depth data. However, relying only on the neural network model to fit the raw experimental data ignores the abundant and effective expert prior knowledge, which will reduce the accuracy of the model, and even lead to wrong prediction results due to the limited amount of data of the training sample, data bad pixels, poor data distribution, etc. To this end, an algorithm for outlier detection of concrete target penetration depth data combined with prior knowledge was proposed. Firstly, the back propagation (BP) neural network model is used to fit the distribution of the experiment samples, then the outlier is screened out based on the deviation index, and at last, the anomaly detection performance of the model is evaluated by the empirical algorithm. Based on the characteristics of the experimental data, the batch gradient descent combined with the momentum optimization method is selected to improve the stability and efficiency during training. Furthermore, by adding domain prior knowledge with the BP neural network model to constrain the fitting of the sample data, the model can reflect the influence of additional features during training. The research results show that the BP neural network model is suitable for the outlier detection of the rigid projectile penetrating concrete experiment data. The fusion of reasonable prior knowledge can improve the detection accuracy and the convergence speed of the model, furthermore, integrating different prior knowledge will cause different results.
- concrete penetration,
- neural network,
- prior knowledge,
- anomaly detection

FullText(HTML)

References(23)

References

[1]	张磊, 吴昊, 赵强, 等. 基于数据挖掘技术的地下工程目标毁伤效应计算方法 [J]. 爆炸与冲击, 2021, 41(3): 031101. DOI: 10.11883/bzycj-2020-0114. ZHANG L, WU H, ZHAO Q, et al. 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.
[2]	LI Q L, WANG Y, SHAO Y D, et al. A comparative study on the most effective machine learning model for blast loading prediction: from GBDT to Transformer [J]. Engineering Structures, 2023, 276: 115310. DOI: 10.1016/j.engstruct.2022.115310.
[3]	ALMUSTAFA M K, NEHDI M L. Machine learning model for predicting structural response of RC slabs exposed to blast loading [J]. Engineering Structures, 2020, 221: 111109. DOI: 10.1016/j.engstruct.2020.111109.
[4]	ALMUSTAFA M K, NEHDI M L. Machine learning model for predicting structural response of RC columns subjected to blast loading [J]. International Journal of Impact Engineering, 2022, 162: 104145. DOI: 10.1016/j.ijimpeng.2021.104145.
[5]	ZHAO C F, ZHU Y F, ZHOU Z H. Machine learning-based approaches for predicting the dynamic response of RC slabs under blast loads [J]. Engineering Structures, 2022, 273: 115104. DOI: 10.1016/j.engstruct.2022.115104.
[6]	NETO L B, SALEH M, PICKERD V, et al. Rapid mechanical evaluation of quadrangular steel plates subjected to localised blast loadings [J]. International Journal of Impact Engineering, 2020, 137: 103461. DOI: 10.1016/j.ijimpeng.2019.103461.
[7]	WANG H Z, BAH M J, HAMMAD M. Progress in outlier detection techniques: a survey [J]. IEEE Access, 2019, 7: 107964–108000. DOI: 10.1109/ACCESS.2019.2932769.
[8]	PANG G S, SHEN C H, CAO L B, et al. Deep learning for anomaly detection: a review [J]. ACM Computing Surveys, 2021, 54(2): 38. DOI: 10.1145/3439950.
[9]	MURALIDHAR N, ISLAM M R, MARWAH M, et al. Incorporating prior domain knowledge into deep neural networks [C]//2018 IEEE International Conference on Big Data. Seattle: IEEE, 2018: 36–45. DOI: 10.1109/BigData.2018.8621955.
[10]	ZHANG W E, SHENG Q Z, ALHAZMI A, et al. Adversarial attacks on deep-learning models in natural language processing: a survey [J]. ACM Transactions on Intelligent Systems and Technology, 2020, 11(3): 24. DOI: 10.1145/3374217.
[11]	VON RUEDEN L, MAYER S, BECKH K, et al. Informed machine learning: a taxonomy and survey of integrating prior knowledge into learning systems [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 614–633. DOI: 10.1109/TKDE.2021.3079836.
[12]	FORRESTAL M J, LUK V K. Dynamic spherical cavity-expansion in a compressible elastic-plastic solid [J]. Journal of Applied Mechanics, 1988, 55(2): 275–279. DOI: 10.1115/1.3173672.
[13]	文鹤鸣. 混凝土靶板冲击响应的经验公式 [J]. 爆炸与冲击, 2003, 23(3): 267–274. WEN H M. Empirical equations for the impact response of concrete targets [J]. Explosion and Shock Waves, 2003, 23(3): 267–274.
[14]	CHEN X W, LI Q M. Deep penetration of a non-deformable projectile with different geometrical characteristics [J]. International Journal of Impact Engineering, 2002, 27(6): 619–637. DOI: 10.1016/S0734-743X(02)00005-2.
[15]	LI Q M, CHEN X W. Dimensionless formulae for penetration depth of concrete target impacted by a non-deformable projectile [J]. International Journal of Impact Engineering, 2003, 28(1): 93–116. DOI: 10.1016/S0734-743X(02)00037-4.
[16]	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.
[17]	陈小伟. 穿甲/侵彻力学的理论建模与分析 [M]. 北京: 科学出版社, 2019.
[18]	王清华, 徐丰, 郭伟国. 基于ANN-GA协同寻优的动态拉伸试样尺寸优化方法 [J]. 爆炸与冲击, 2022, 42(1): 014201. DOI: 10.11883/bzycj-2021-0218. WANG Q H, XU F, GUO W G. A method of geometry optimization for dynamic tensile specimen based on artificial neural network and genetic algorithm [J]. Explosion and Shock Waves, 2022, 42(1): 014201. DOI: 10.11883/bzycj-2021-0218.
[19]	李秦超, 姚成宝, 程帅, 等. 神经网络状态方程在强爆炸冲击波数值模拟中的应用 [J]. 爆炸与冲击, 2023, 43(4): 044202. DOI: 10.11883/bzycj-2022-0222. LI Q C, YAO C B, CHENG S, et al. Application of the neural network equation of state in numerical simulation of intense blast wave [J]. Explosion and Shock Waves, 2023, 43(4): 044202. DOI: 10.11883/bzycj-2022-0222.
[20]	MUSTAPHA A, MOHAMED L, ALI K. An overview of gradient descent algorithm optimization in machine learning: Application in the ophthalmology field [C]//The 3rd International Conference on Smart Applications and Data Analysis. Marrakesh, Morocco: Springer, 2020: 349–359. DOI: 10.1007/978-3-030-45183-7_27.
[21]	ZHANG L, WANG J M, JIANG M W, et al. Evaluation method based on random forests for empirical algorithms of penetration effects [C]//2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM). Xiamen, Fujian, China: IEEE, 2022: 73–78. DOI: 10.1109/MLCCIM55934.2022.00020.
[22]	FULLARD K, BARR P. Development of design guidance for low velocity impacts on concrete floors [J]. Nuclear Engineering and Design, 1989, 115(1): 113–120. DOI: 10.1016/0029-5493(89)90264-1.
[23]	YOUNG C W. Penetration equations [R]. Albuquerque, NM, United States: Sandia National Lab, 1997. DOI: 10.2172/562498.