Damage prediction of reinforced concrete slab under underwater near-field explosion based on machine learning

XUE Zhengchun; Chen Jianyu; SHENG Longhan

doi:10.11993/j.issn.2096-3920.2025-0059

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2025 > Accepted Manuscript

XUE Zhengchun, Chen Jianyu, SHENG Longhan. Damage prediction of reinforced concrete slab under underwater near-field explosion based on machine learning[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0059

Citation:

XUE Zhengchun, Chen Jianyu, SHENG Longhan. Damage prediction of reinforced concrete slab under underwater near-field explosion based on machine learning[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0059

Citation:

PDF( 2281 KB)

Damage prediction of reinforced concrete slab under underwater near-field explosion based on machine learning

doi: 10.11993/j.issn.2096-3920.2025-0059

Nanjing University of Science and Technology, Nan′jing 210094, China

Received Date: 2025-04-21
Accepted Date: 2025-06-13
Rev Recd Date: 2025-06-11

Available Online: 2025-10-14

Abstract

Abstract

Reinforced concrete (RC) slabs are typical load-bearing components of underwater structures, and accurately predicting their damage under underwater explosions remains a critical challenge in protective engineering. Such prediction is essential for structural health monitoring, blast-resistant design, and safety assessment. This study proposes a novel hybrid damage-prediction approach that integrates numerical simulation with machine learning. First, a fluid–structure coupling model was established in LS-DYNA using the Arbitrary Lagrangian–Eulerian (ALE) algorithm to simulate the dynamic response and damage evolution of concrete slabs subjected to different explosive charges and stand-off distances. Subsequently, a deep neural network (DNN)-based prediction model was developed, and its accuracy was significantly improved (exceeding 98%) through optimization of the hidden-layer architecture and neuron configuration, effectively avoiding overfitting. Furthermore, a convolutional neural network (CNN) was introduced for automatic recognition and quantification of damage regions from simulation images, greatly enhancing the efficiency of damage prediction. The results reveal that the damaged areas exhibit geometric regularity within specific loading conditions. The proposed methodology provides a new perspective for underwater explosion damage assessment and offers valuable guidance for the design of protective engineering structures.
- reinforced concrete slab,
- numerical simulation,
- machine learning,
- neural network,
- damage prediction

FullText(HTML)

References(26)

References

[1]	马上, 王振清, 陈叶青, 等. 水中爆炸作用下混凝土重力坝损伤研究[J]. 混凝土, 2023(11): 63-71. MA S, WANG Z Q, CHEN Y Q, et al. Study on damage of concrete gravity dam under underwater explosion loading[J]. Concrete, 2023(11): 63-71.
[2]	COLE R H. Underwater explosions[M]. Princeton: Princeton University Press, 1948.
[3]	WANG J, GUO J, YAO X L, et al. Dynamic buckling of stiffened plates subjected to explosion impact loads[J]. Shock Waves, 2017, 27: 37-52. doi: 10.1007/s00193-016-0638-z
[4]	HOLMQUIST T J. , JOHNSON G R. , COOK W H. A computational constitutive model for concrete subjected to large strains, high strain rates, and high pressures[J]. Proceedings of the Fourteenth International Symposium on Ballistics, 1993, 1, 591-600.
[5]	周飞, 李贺东, 吴昊. 水中近场爆炸下钢筋混凝土板局部破坏模式数值模拟研究[J]. Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2024, 25(08): 650-669. ZHOU, F. , LI, H. , WU, H. , & ZHOU, A. F. (2024). Numerical simulation study on local failure mode of reinforced concrete slabs under near-field explosion in water[J]. Journal of Zhejiang University SCIENCE A, 25(8), 650-669.
[6]	HOLMQUIST T J, JOHNSON G R. A computational constitutive model for glass subjected to large strains, high strain rates and high pressures[J]. Journal of Applied Mechanics, 2011, 109(12): 051003.
[7]	刘丽滨, 李海涛, 刁爱民, 等. 水下爆炸船体梁总体响应特性数值模拟[J]. 高压物理学报, 2021, 35(6): 178-185. LIU L B, LI H T, DIAO A M, et al. Numerical simulation of overall response characteristics of ship beams under underwater explosion[J]. Chinese Journal of High Pressure Physics, 2021, 35(6): 178-185.
[8]	朱玉富, 赵春风, 周志航. 基于机器学习的钢筋混凝土板在爆炸作用下的最大位移预测模型[J]. 高压物理学报, 2023, 37(2): 92-106. ZHU Y F, ZHAO C F, ZHOU Z H. Maximum displacement prediction model of reinforced concrete slab under blast loading based on machine learning[J]. Chinese Journal of High Pressure Physics, 2023, 37(2): 92-106.
[9]	Li Q L, Wang Z T, Chen W S, et al. Advancing blast fragmentation simulation of RC slabs: A graph neural network approach[J]. Engineering Structures, 2024, 308: 118009. doi: 10.1016/j.engstruct.2024.118009
[10]	FARIS H. MIRJALILI S. ALJARAH I. Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme[J]. International Journal of Machine Learning and Cybernetics, 2019, 10: 2901-2920. doi: 10.1007/s13042-018-00913-2
[11]	赵春风, 向思麒, 朱玉富. 基于机器学习的RC板爆炸两阶段损伤评估模型[J]. 建筑结构学报, 2025, 46(1): 212-222. ZHAO C F, XIANG S Q, ZHU Y F. Two-stage damage assessment model for RC slabs under blast loading based on machine learning[J]. Journal of Building Structures, 2025, 46(1): 212-222.
[12]	WANG W, ZHANG D, LU F Y, et al. Experimental study on scaling the explosion resistance of a oneway square reinforced concrete slab under a close-in blast loading[J]. International Journal of Impact Engineering, 2012, 49: 158-164. doi: 10.1016/j.ijimpeng.2012.03.010
[13]	刘靖晗, 唐廷, 韦灼彬, 等. 水下爆炸作用下高桩码头损伤特性数值模拟研究[J]. 兵器装备工程学报, 2024, 45(10): 53-60. LIU J H, TANG T, WEI Z B, et al. Numerical simulation of damage characteristics of high-pile wharf under underwater explosion[J]. Journal of Ordnance Equipment Engineering, 2024, 45(10): 53-60.
[14]	胡锦华. 水下接触爆炸下钢筋混凝土板毁伤模式探究[J]. 工程爆破, 2022, 28(3): 17-23. HU J H. Study on damage modes of reinforced concrete slabs under underwater contact explosion[J]. Engineering Blasting, 2022, 28(3): 17-23.
[15]	BILLINGS, S. A. , & VOON, W. S. F. Least squares parameter estimation algorithms for non-linear systems. International[J] Journal of Systems Science, 1984, 15(6), 601-615.
[16]	HAGAN M T, MENHAJ M B. Training feed forward networks with the Marquardt algorithm[J]. IEEE transactions on Neural Networks, 1994, 5(6): 989-993. doi: 10.1109/72.329697
[17]	RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. nature, 1986, 323(6088): 533-536. doi: 10.1038/323533a0
[18]	He K, Zhang X, Ren S, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]//015 IEEE International Conference on Computer Vision(ICCV). Santiago, Chile: ICCV, 2015: 1026-1034.
[19]	SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural networks, 2015, 61: 85-117. doi: 10.1016/j.neunet.2014.09.003
[20]	ZHANG, W. NIU, L. ZHANG, D. et al. Hw-adam: Fpga-based accelerator for adaptive moment estimation[J]. Electronics, 2023, 12(2): 263. doi: 10.3390/electronics12020263
[21]	WIANGKHAM A, ARIYARIT A, AENGCHUAN P. Prediction of the influence of loading rate and sugarcane leaves concentration on fracture toughness of sugarcane leaves and epoxy composite using artificial intelligence[J]. Theoretical and Applied Fracture Mechanics, 2022, 117: 103188. doi: 10.1016/j.tafmec.2021.103188
[22]	REN S F, ZHAO P F, WANG S P, et al. Damage prediction of stiffened plates subjected to underwater contact explosion using the machine learning-based method[J]. Ocean Engineering, 2022, 266: 112839. doi: 10.1016/j.oceaneng.2022.112839
[23]	于玲玲, 张飞, 何文凯. 考虑地应力与先爆孔损伤的光面爆破数值模拟[J]. 地下空间与工程学报, 2024, 20(2): 636-644. YU L L, ZHANG F, HE W K. Numerical simulation of smooth blasting considering in-situ stress and pre-blast hole damage[J]. Chinese Journal of Underground Space and Engineering, 2024, 20(2): 636-644.
[24]	PENG X, HU Z, LI T. Neural network based combining prediction model and its application in ship motion prediction[C]//2010 8th World Congress on Intelligent Control and Automation. Jinan, China: IEEE, 2010: 5624-5627.
[25]	HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647
[26]	SRIVASTAVA N. HINTON G E, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal Of Machine Learning Research, 2014, 15(1): 1929-1958.