Damage Prediction of Reinforced Concrete Slab under Underwater Near-Field Explosion Based on Machine Learning

XUE Zhengchun; Chen Jianyu; SHEN Longhan

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

Volume 33 Issue 5

Oct 2025

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XUE Zhengchun, Chen Jianyu, SHEN Longhan. Damage Prediction of Reinforced Concrete Slab under Underwater Near-Field Explosion Based on Machine Learning[J]. Journal of Unmanned Undersea Systems, 2025, 33(5): 856-864. doi: 10.11993/j.issn.2096-3920.2025-0059

Citation:

XUE Zhengchun, Chen Jianyu, SHEN Longhan. Damage Prediction of Reinforced Concrete Slab under Underwater Near-Field Explosion Based on Machine Learning[J]. Journal of Unmanned Undersea Systems, 2025, 33(5): 856-864. doi: 10.11993/j.issn.2096-3920.2025-0059

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Damage Prediction of Reinforced Concrete Slab under Underwater Near-Field Explosion Based on Machine Learning

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

School of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing 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 research challenge in protective engineering. Such prediction is essential for structural health monitoring, blast-resistant design, and safety assessment. This study proposed a damage prediction approach that integrated numerical simulation with machine learning. First, a fluid-structure coupling model was established in LS-DYNA software, and the arbitrary Lagrangian-Eulerian(ALE) algorithm was used to simulate the dynamic response and damage range of RC 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 of damage images, greatly enhancing the efficiency of damage prediction. The results reveal that the damaged areas of RC slabs exhibit geometric regularity within a certain range. 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,
- underwater near-field explosion,
- machine learning,
- neural network,
- damage prediction

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References(26)

References

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