A physics-information and data fusion-driven method for rapid prediction of blast loads in complex urban environments

HUANG Yang; LUO Dingkun; CHEN Suwen

doi:10.11883/bzycj-2025-0238

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HUANG Yang, LUO Dingkun, CHEN Suwen. A physics-information and data fusion-driven method for rapid prediction of blast loads in complex urban environments[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0238

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HUANG Yang, LUO Dingkun, CHEN Suwen. A physics-information and data fusion-driven method for rapid prediction of blast loads in complex urban environments[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0238

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A physics-information and data fusion-driven method for rapid prediction of blast loads in complex urban environments

doi: 10.11883/bzycj-2025-0238

HUANG Yang^1
,,
LUO Dingkun¹,
CHEN Suwen^{1, 2
,
,}

1.
College of Civil Engineering, Tongji University, Shanghai 200092, China
2.
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China

Received Date: 2025-08-08
Rev Recd Date: 2025-10-10

Available Online: 2025-10-11

Abstract

Abstract

Rapid and accurate assessment of blast loads in complex urban blocks is critical for efficient blast-resistant structural design and post-disaster damage evaluation. However, traditional methods, including empirical formulas, physical models, and numerical simulations, struggle to simultaneously achieve high computational efficiency and prediction accuracy. Furthermore, existing deep learning-based blast load prediction models are hard to be applied in complex urban block scenarios. To achieve rapid and accurate assessment of blast loads in complex urban street blocks, a physics-information and data fusion-driven method is proposed. The core idea of the method is a “spatial partitioning and progressive inference” strategy, which involves constructing distinct rapid prediction models for “the detonation street” and “non-detonation streets”. These models then collaborate synergistically via their shared boundary pressures to predict the spatiotemporal evolution of the pressure field across the entire urban block. The two network models incorporate the results from method of images, signed distance fields, and energy density factors to integrate key physical features of the flow field. For the architectures, the two models adopt a 3D-UNet and a cascaded network composed of a 2D-UNet and a 3D-UNet, respectively. The target outputs for both networks were generated using a validated numerical simulation method, which were then used to train the models. Evaluation of the model’s predictive performance demonstrates that the proposed method accurately predicts the spatiotemporal evolution of the pressure field. The relative error between the predicted flow field and numerical simulation results is within 20% in both detonation and non-detonation streets. Moreover, the method effectively captures the pressure-time histories at specified locations. The inference time of the proposed dual-network collaborative method is approximately 2% of the computation time of the corresponding numerical simulation, and the flow field storage cost for a single time step is less than 0.2% of a D3PLOT file, thereby significantly reducing computational and storage costs. The research provides a novel method for the rapid assessment of blast loads in large-scale, complex urban blocks, offering efficient decision-making support for the blast-resistant design and evaluation of urban buildings.
- complex urban environment,
- explosion load,
- method of images,
- energy density factor,
- deep learning

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References

[1]	SMITH P D, WHALEN G P, FENG L J, et al. Blast loading on buildings from explosions in city streets [J]. Proceedings of the Institution of Civil Engineers - Structures and Buildings, 2001, 146(1): 47–55. DOI: 10.1680/stbu.2001.146.1.47.
[2]	ROSE T A, SMITH P D. Influence of the principal geometrical parameters of straight city streets on positive and negative phase blast wave impulses [J]. International Journal of Impact Engineering, 2002, 27(4): 359–376. DOI: 10.1016/S0734-743X(01)00060-4.
[3]	SMITH P D, ROSE T A. Blast wave propagation in city streets—an overview [J]. Progress in Structural Engineering and Materials, 2006, 8(1): 16–28. DOI: 10.1002/pse.209.
[4]	FOUCHIER C, LABOUREUR D, YOUINOU L, et al. Experimental investigation of blast wave propagation in an urban environment [J]. Journal of Loss Prevention in the Process Industries, 2017, 49: 248–265. DOI: 10.1016/j.jlp.2017.06.021.
[5]	REMENNIKOV A M. Evaluation of blast loads on buildings in urban environment [M]//JONES N, BREBBIA C A. Structures under Shock and Impact VIII. UK: WIT Press, 2004: 73–82.
[6]	REMENNIKOV A M, ROSE T A. Modelling blast loads on buildings in complex city geometries [J]. Computers & Structures, 2005, 83(27): 2197–2205. DOI: 10.1016/j.compstruc.2005.04.003.
[7]	ZHOU X Q, HAO H. Prediction of airblast loads on structures behind a protective barrier [J]. International Journal of Impact Engineering, 2008, 35(5): 363–375. DOI: 10.1016/j.ijimpeng.2007.03.003.
[8]	US Department of Defense. UFC 3-340-02 Structures to resist the effects of accidental explosions [S]. Washington: The US Department of Army, 2008.
[9]	BAKER W E, COX P A, WESTINE P S, et al. Explosion hazards and evaluation [M]. Amsterdam: Elsevier Scientific Pub. Co. , 1983: 111–209.
[10]	NEEDHAM C E. Blast waves [M]. Cham: Springer, 2018: 363–380.
[11]	CHAN P C, KLEIN H H. A study of blast effects inside an enclosure [J]. Journal of Fluids Engineering, 1994, 116(3): 450–455. DOI: 10.1115/1.2910297.
[12]	杨亚东, 李向东, 王晓鸣. 长方体密闭结构内爆炸冲击波传播与叠加分析模型 [J]. 兵工学报, 2016, 37(8): 1449–1455. DOI: 10.3969/j.issn.1000-1093.2016.08.016. YANG Y D, LI X D, WANG X M. An analytical model for propagation and superposition of internal explosion shockwaves in closed cuboid structure [J]. Acta Armamentarii, 2016, 37(8): 1449–1455. DOI: 10.3969/j.issn.1000-1093.2016.08.016.
[13]	REMENNIKOV A M, MENDIS P A. Prediction of airblast loads in complex environments using artificial neural networks [M]//JONES N, BREBBIA C A. Ninhth International Conference on Structures under Shock and Impact. UK: Wessex Institute of Technology, 2006: 269–278.
[14]	DENNIS A A, RIGBY S E. The direction-encoded neural network: a machine learning approach to rapidly predict blast loading in obstructed environments [J]. International Journal of Protective Structures, 2024, 15(3): 455–483. DOI: 10.1177/20414196231177364.
[15]	杜晓庆, 何益平, 邱涛, 等. 基于PCA-BPNN的桥梁爆炸荷载时程预测 [J]. 爆炸与冲击, 2025, 45(3): 033201. DOI: 10.11883/bzycj-2023-0343. DU X Q, HE Y P, QIU T, et al. Prediction of blast loads on bridge girders based on PCA-BPNN [J]. Explosion and Shock Waves, 2025, 45(3): 033201. DOI: 10.11883/bzycj-2023-0343.
[16]	KANG M A, PARK C H. Prediction of peak pressure by blast wave propagation between buildings using a conditional 3D convolutional neural network [J]. IEEE Access, 2023, 11: 26114–26124. DOI: 10.1109/ACCESS.2023.3257345.
[17]	HUANG Y, ZHU S J, CHEN S W. Deep learning-driven super-resolution reconstruction of two-dimensional explosion pressure fields [J]. Journal of Building Engineering, 2023, 78: 107620. DOI: 10.1016/j.jobe.2023.107620.
[18]	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.
[19]	LI Q L, WANG Y, CHEN W S, et al. Machine learning prediction of BLEVE loading with graph neural networks [J]. Reliability Engineering & System Safety, 2024, 241: 109639. DOI: 10.1016/j.ress.2023.109639.
[20]	李般若, 霍璞, 喻君. 基于GNN的爆炸压力时空分布预测模型 [J/OL]. 爆炸与冲击, (2025-06-16)[2025-08-08]. https://link.cnki.net/urlid/51.1148.O3.20250616.1620.032. DOI: 10.11883/bzycj-2024-0503. LI B R, HUO P, YU J. GNN-based predictive model for spatial and temporal distribution of blast overpressure [J/OL]. Explosion and Shock Waves, (2025-06-16)[2025-08-08]. https://link.cnki.net/urlid/51.1148.O3.20250616.1620.032. DOI: 10.11883/bzycj-2024-0503.
[21]	WANG Z Q, PENG J Z, HU J, et al. BlastGraphNet: an intelligent computational method for the precise and rapid prediction of blast loads on complex 3D buildings using graph neural networks [J]. Engineering, 2025, 49: 205–224. DOI: 10.1016/j.eng.2025.03.007.
[22]	HUANG Y, CHEN S W, CHEN X. Pressure–time history prediction for fully and partially confined explosions by combining physical and deep learning models [J]. Engineering Structures, 2025, 339: 120575. DOI: 10.1016/j.engstruct.2025.120575.
[23]	ОРЛЕНКО Л П. 爆炸物理学 [M]. 孙承纬, 译. 北京: 科学出版社, 2011: 101–341.
[24]	HENRYCH. The dynamics of explosion and its use [M]. Amsterdam: Elsevier, 1979: 149–177.
[25]	WU C Q, HAO H. Modeling of simultaneous ground shock and airblast pressure on nearby structures from surface explosions [J]. International Journal of Impact Engineering, 2005, 31(6): 699–717. DOI: 10.1016/j.ijimpeng.2004.03.002.
[26]	LI Y F, CHANG J T, WANG Z A, et al. An efficient deep learning framework to reconstruct the flow field sequences of the supersonic cascade channel [J]. Physics of Fluids, 2021, 33(5): 056106. DOI: 10.1063/5.0048170.
[27]	ÇIÇEK Ö, ABDULKADIR A, LIENKAMP S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation [C]//Proceedings of 19th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Athens: Springer, 2016: 424–432. DOI: 10.1007/978-3-319-46723-8_49.
[28]	RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation [C]//Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Munich: Springer, 2015: 234–241. DOI: 10.1007/978-3-319-24574-4_28.
[29]	SILVESTRINI M, GENOVA B, LEON TRUJILLO F J. Energy concentration factor. A simple concept for the prediction of blast propagation in partially confined geometries [J]. Journal of Loss Prevention in the Process Industries, 2009, 22(4): 449–454. DOI: 10.1016/j.jlp.2009.02.018.
[30]	钟巍, 田宙, 寿列枫. 基于能量密度因子法的复杂环境下爆炸冲击波超压峰值解析计算 [J]. 科学技术与工程, 2021, 21(28): 11947–11954. DOI: 10.3969/j.issn.1671-1815.2021.28.007. ZHONG W, TIAN Z, SHOU L F. Analytical calculation of the blast wave peak overpressure under complex environments based on the energy concentration factor method [J]. Science Technology and Engineering, 2021, 21(28): 11947–11954. DOI: 10.3969/j.issn.1671-1815.2021.28.007.
[31]	FEDOROVA N N, VALGER S A, FEDOROV A V. Simulation of blast action on civil structures using ANSYS Autodyn [J]. AIP Conference Proceedings, 2016, 1770(1): 020016. DOI: 10.1063/1.4963939.
[32]	甘露, 陈力, 宗周红, 等. 近距离爆炸比例爆距的界定标准及荷载模型 [J]. 爆炸与冲击, 2021, 41(6): 064902. DOI: 10.11883/bzycj-2020-0194. GAN L, CHEN L, ZONG Z H, et al. Definition of scaled distance of close-in explosion and blast load calculation model [J]. Explosion and Shock Waves, 2021, 41(6): 064902. DOI: 10.11883/bzycj-2020-0194.
[33]	Federal Emergency Management Agency. Incremental protection for existing commercial buildings from terrorist attack [S]. Washington: Department of Homeland Security, Science and Technology Directorate, 2008.
[34]	KONG X S, ZHOU H, XU J B, et al. Scaling of confined explosion and structural response [J]. Thin-Walled Structures, 2023, 186: 110656. DOI: 10.1016/j.tws.2023.110656.