Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks

Peiji HUANG; Weiwen PENG; Chunjiang LENG; Qing ZHANG; Wei ZHONG

doi:10.12382/bgxb.2024.0987

您当前的位置：

首页 >

文章列表页 >

Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks

更新时间：2025-09-25

- Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks
- Acta Armamentarii Vol. 46, Issue 8, Pages: 240987(2025)
- 作者机构：
  
  1. 中山大学智能工程学院, 广东广州 510275
  2. 西北核技术研究所强脉冲辐射环境模拟与效应全国重点实验室, 陕西西安 710024
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0987
  CLC：
- Received：24 October 2024，
  
  Published Online：28 August 2025，
  
  Published：31 August 2025
- 稿件说明：
移动端阅览
Peiji HUANG, Weiwen PENG, Chunjiang LENG, et al. Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks[J]. Acta Armamentarii, 2025, 46(8): 240987.
DOI：

Peiji HUANG, Weiwen PENG, Chunjiang LENG, et al. Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks[J]. Acta Armamentarii, 2025, 46(8): 240987. DOI： 10.12382/bgxb.2024.0987.

摘要

城市建筑的交错布局提高了爆炸冲击波传播路径的复杂性

从而增加了对毁伤效应进行全面准确评估的难度。基于计算流体动力学的数值模拟方法虽然能够精确模拟爆炸产生的冲击载荷

但所需的计算量大且计算时间长。为实现在密集城市建筑群场景下爆炸冲击波载荷的快速预测

提出基于神经网络的快速预测方法。分析训练样本数量对模型预测精度的影响

研究区域划分对模型预测性能的作用。为满足神经网络模型训练的数据需求

使用爆炸模拟软件在一个典型密集城市建筑群场景下进行网格敏感性分析

在兼顾模拟速度和精度情况下产生了80组爆炸情况的数据;为确定合适的模型结构

构建不同层数的全连接神经网络进行对比实验与分析;通过对比实验

分析训练样本数量、区域划分以及构建双模型对模型预测精度的影响。实验结果表明:新方法在训练数据以外未见过的16组测试数据上预测误差达到10%以内

推理时间只需2s

为实现城市建筑群爆炸载荷快速预测提供了一种新的途径和视角。

Abstract

The staggered layout of urban building complex makes the propagation path of explosion shock wave more complicated

thereby increasing the difficulty of evaluating the damage effect comprehensively and accurately.Numerical simulation methods based on computational fluid dynamics can accurately simulate the blast loading

but the calculated amount is large and the calculation time is long.In order to rapidly predict the blast loading in urban building complex

a fast blast loading prediction method based on neural network is proposed.The influence of the number of training samples on the prediction accuracy and the effect of regional division on the prediction performance of the model are analyzed.In order to meet the data requirements for training the neural network model

the explosion simulation software is used to analyze the mesh sensitivity in a typical dense urban building complex and generate a dataset for 80 sets of explosion scenarios while considering simulation speed and accuracy.In order to determine the appropriate model structure

the fully connected neural networks with different numbers of layers are constructed for comparative experiment and analysis.The effects of the number of training samples

the division of region and the construction of dual models on the prediction accuracy of the model are analyzed through comparative experiments.The results show that the prediction error of the proposed method is less than 10% on 16 sets of test data except the training data

and the inference time only takes 2 seconds.The proposed method has a balanced and good prediction ability for various ranges of peak overpressure

and provides a new approach and perspective for realizing the rapid prediction of blast loading in urban building complex.

关键词

Keywords

references

RIGBY S E , LODGE T J , ALOTAIBI S , et al . Preliminary yield estimation of the 2020 Beirut explosion using video footage from social media [J ] . Shock Waves , 2020 , 30 ( 6 ): 671 - 675 .

曹涛 , 孙浩 , 周游 , 等 . 近地爆炸冲击波传播特性数值模拟与应用 [J ] . 兵器装备工程学报 , 2020 , 41 ( 12 ): 187 - 191 .

CAO T , SUN H , ZHOU Y , et al . Numerical simulation and application of propagation characteristics of shock wave near ground explosion [J ] . Journal of Ordnance Equipment Engineering , 2020 , 41 ( 12 ): 187 - 191 . (in Chinese)

张云峰 , 陈博 , 魏欣 , 等 . 空气自由场爆炸冲击波数值建模及应用 [J ] . 爆炸与冲击 , 2023 , 43 ( 11 ): 114202 .

ZHANG Y F , CHEN B , WEI X , et al . Numerical modeling and application of shock wave of free-field air explosion [J ] . Explosion and Shock Waves , 2023 , 43 ( 11 ): 114202 . (in Chinese)

张晓颖 , 李胜杰 , 李志强 . 爆炸载荷作用下夹层玻璃动态响应的数值模拟 [J ] . 兵工学报 , 2018 , 39 ( 7 ): 1379 - 1388 . DOI: 10.3969/j.issn.1000-1093.2018.07.016 http://doi.org/10.3969/j.issn.1000-1093.2018.07.016 为研究爆炸载荷作用下夹层玻璃的动态响应，利用有限元软件LS-DYNA对爆炸载荷作用下夹层玻璃的动态响应进行了数值模拟。通过改变外层玻璃与内层玻璃的厚度，系统地研究不同组合下夹层玻璃的动态响应规律，描述爆炸产物与结构相互作用过程，分析夹层玻璃不同部分的能量吸收效率，观察夹层玻璃的裂纹扩展过程。结果表明：玻璃厚度的改变对结构动态响应有明显影响，随着爆炸距离增加，影响程度逐渐减小；爆炸产物先于空气冲击波对玻璃的冲击有损伤破坏作用；结构外层玻璃的能量吸收效率最大，聚乙烯醇缩丁醛胶层次之，内层玻璃吸收效率最小；爆炸载荷下夹层玻璃的裂纹以环向裂纹为主，径向裂纹相对较少。

ZHANG X Y , LI S J , LI Z Q . Numerical simulation of dynamic response of laminated glass subjected to blast load [J ] . Acta Armamentarii , 2018 , 39 ( 7 ): 1379 - 1388 . (in Chinese)

赵海涛 , 王成 . 空中爆炸问题的高精度数值模拟研究 [J ] . 兵工学报 , 2013 , 34 ( 12 ): 1536 - 1546 . DOI: 10.3969/j.issn.1000-1093.2013.12.008 http://doi.org/10.3969/j.issn.1000-1093.2013.12.008 针对空中爆炸初期强间断和爆炸后期接触间断物理特性，提出了虚拟流体方法（GFM）和真实虚拟流体方法（RGFM）2种界面处理方法相耦合的计算方法。在高密度比、高压力比同时存在的爆炸初期和压力、密度及速度等物理量相接近的爆炸后期，分别采用RGFM和GFM对物质界面两侧物理量进行处理。采用Local Level Set方法对运动界面进行追踪，并用5阶高精度加权本质非振荡（WENO）格式和3阶TVD Runge-Kutta方法对控制方程进行离散，编制了空中爆炸数值模拟程序，应用该程序对不同高度近地面空中爆炸以及冲击波与挡墙相互作用问题进行数值模拟，模拟结果能够较好地反映空中爆炸中冲击波的产生、传播、反射、绕射及爆炸产物的膨胀等现象，并与经验公式和试验结果吻合较好。证明了该耦合方法能够模拟空中爆炸问题，并且爆炸波在传播过程中具有很好的对称性，为模拟高密度比、高压力比的多物质之间相互作用问题提供了有效的计算方法。

ZHAO H T , WANG C . High resolution numerical simulation of air explosion [J ] . Acta Armamentarii , 2013 , 34 ( 12 ): 1536 - 1546 . (in Chinese)

SHI Y C , LIU S Z , LI Z X , et al . Review on quick safety assessment of building structures in complex urban environment after extreme explosion events [J ] . International Journal of Protective Structures , 2023 , 14 ( 3 ): 438 - 458 .

REMENNIKOV A M , MENDIS P A . Prediction of airblast loads in complex environments using artificial neural networks [J ] . WIT Transactions on The Built Environment , 2006 , 87 : 269 - 278 .

FLOOD I , BEWICK B T , RAUCH E . Rapid simulation of blast wave propagation in built environments using coarse-grain simulation [J ] . International Journal of Protective Structures , 2012 , 3 ( 4 ): 431 - 448 .

PANNELL J J , RIGBY S E , PANOUTSOS G . Physics-informed regularisation procedure in neural networks:an application in blast protection engineering [J ] . International Journal of Protective Structures , 2022 , 13 ( 3 ): 555 - 578 .

PANNELL J J , RIGBY S E , PANOUTSOS G . Application of transfer learning for the prediction of blast impulse [J ] . International Journal of Protective Structures , 2023 , 14 ( 2 ): 242 - 262 .

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 .

LI Q L , WANG Y , LI L , et al . Prediction of BLEVE loads on structures using machine learning and CFD [J ] . Process Safety and Environmental Protection , 2023 , 171 : 914 - 925 .

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 .

杨森 . 城市建筑群爆炸荷载预测及灾害效应快速评估 [D ] . 天津 : 天津大学 , 2021 .

YANG S . Prediction of blast loads and rapid evaluation of disaster effects in complex urban environments [D ] . Tianjin : Tianjin University , 2021 . (in Chinese)

陈梓薇 , 王仲琦 , 曾令辉 . 基于BP神经网络的爆炸用激波管峰值压力预测方法 [J ] . 爆炸与冲击 , 2024 , 44 ( 5 ): 054101 .

CHEN Z W , WANG Z Q , ZENG L H . A method for predicting peak pressure in an explosion shock tube based on BP neural network [J ] . Explosion and Shock Waves , 2024 , 44 ( 5 ): 054101 . (in Chinese)

LI Q L , LI L , SHAO Y D , et al . A multi-task machine learning approach for data efficient prediction of blast loading [J ] . Engineering Structures , 2025 , 326 : 119577 .

SHI J , LI J , TAM W C , et al . Physics_GNN:Towards physics-informed graph neural network for the real-time simulation of obstructed gas explosion [J ] . Reliability Engineering & System Safety , 2025 , 256 : 110777 .

徐永康 , 薛琨 . 基于人工神经网络算法的多相云雾爆轰毁伤效应预测模型 [J ] . 兵工学报 , 2024 , 45 ( 6 ): 1889 - 1905 . DOI: 10.12382/bgxb.2023.0094 http://doi.org/10.12382/bgxb.2023.0094 云雾爆轰毁伤范围的预测是云爆武器造成大规模毁伤的研究基础,但云雾爆轰后超压场分布规律与燃料浓度的依赖关系未知,制约了对于云爆毁伤范围的预测。因此,针对2种最为常用的多相云雾燃料,采用最小自由能法计算得到了液相燃料完全以液滴或蒸汽形式存在的多相云雾发生理想爆轰的CJ参数,并通过拟合,得到体JWL状态方程参数。在此基础上计算得到了不同浓度和状态的多相云雾理想爆轰造成的超压场,并借助人工神经网络获得了浓度范围在0.03~0.30kg/m<sup>3</sup>的气固两相和气液固三相云雾场爆轰峰值超压随比例距离衰减规律的代理模型并预测不同毁伤等级对应的毁伤比例半径随燃料浓度的变化,得到毁伤比例半径最大的最优浓度。研究结果表明:云雾区中液相燃料以液滴或蒸汽形式存在对云雾爆轰参数,产物JWL状态方程参数,与云爆爆轰后超压场分布规律的影响都比较微弱(<1.5%);在0.03~0.18kg/m<sup>3</sup>的燃料浓度范围内,Ⅰ级~Ⅲ级毁伤比例半径的最大和最小值分别相差21%、19%、6%,因此大装药结构形成的云雾场爆轰后,Ⅰ级和Ⅱ级毁伤半径与燃料浓度的依赖性更强。

XU Y K , XUE K . Artificial neural network-based prediction model for damage effect of fuel-air explosive [J ] . Acta Armamentarii , 2024 , 45 ( 6 ): 1889 - 1905 . (in Chinese) DOI: 10.12382/bgxb.2023.0094 http://doi.org/10.12382/bgxb.2023.0094 The prediction of damage range caused by Fuel-air Explosive is fundamental to the study of large-scale damage caused by Fuel-air Explosive weapons. However, the distribution pattern of shock waves after detonation and its dependenc e on fuel concentration are unknown, which limits the prediction accuracy of damage range. In this study, the minimum free energy method is used to calculate the CJ parameters for the ideal detonation of biphasic cloud fog with liquid fuel present in either droplet or vapor form. The JWL equation of state parameters are obtained through fitting. Subsequently, the peak overpressure caused by ideal detonation of biphasic cloud fog with different concentrations and states is calculated. A proxy model is developed by utilizing an artificial neural network. The proposed model is used to predict the decay law of peak overpressure with respect to the scaled distance for biphasic gas-solid and gas-liquid-solid cloud detonations with concentrations ranging from 0.03 to 0.30kg/m 3 . The model is also used to predict the variation of damage proportion radius with fuel concentration for different damage levels, obtaining the optimal concentration with the maximum damage proportion radius. The study reveals that the influences of liquid fuel in droplet or vapor form on cloud detonation parameters, JWL equation of state parameters, and shock wave distribution after cloud detonation are relatively weak ( < 1.5%). Within the fuel concentrations ranging from 0.03 to 0.18kg/m 3 , the maximum and minimum values of damage proportion radius for damage levels Ⅰ-Ⅲ are differed by 21%, 19%, and 6%, respectively. Thus, the dependence of damage radii on fuel concentration is stronger for damage levels Ⅰ and Ⅱ after cloud burst caused by large explosive structures.

SHENG K K , LI Y , XU X , et al . Stator and rotor temperature prediction of permanent magnet synchronous motor based on BP neural network [C ] // Proceedings of the 2024 43rd Chinese Control Conference . Kunming,China : IEEE , 2024 : 8832 - 8837 .

ZUO K J , YE Z Y , BU S H , et al . Fast simulation of airfoil flow field via deep neural network [J ] . Aerospace Science and Technology , 2024 , 150 : 109207 .

BRITTLE M A . Blast propagation in a geometrically complex environment [D ] . Cranfield,UK : Cranfield University , 2004 .

VALGER S A , FEDOROVA N N , FEDOROVA A V . Numerical simulation of blast action on civil structures in urban environment [J ] . IOP Conference Series:Materials Science and Engineering . IOP Publishing , 2017 , 245 ( 6 ): 062018 .

HEYLMUN J , VONK P , BREWER T . Blastfoam theory and user guide [Z/OL ] . Brazos Street Austin,TX,US : Synthetik Applied Technologies , 2019 ( 2019-10-30 ). www.blastfoam.org/blastFoam_User_Guide.pdf. www.blastfoam.org/blastFoam_User_Guide.pdf www.blastfoam.org/blastFoam_User_Guide.pdf

JASAK H . OpenFOAM:open source CFD in research and industry [J ] . International Journal of Naval Architecture and Ocean Engineering , 2009 , 1 ( 2 ): 89 - 94 .

LI J , HAO H . Numerical and analytical prediction of pressure and impulse from vented gas explosion in large cylindrical tanks [J ] . Process Safety and Environmental Protection , 2019 , 127 : 226 - 244 .

REMENNIKOV A , GAN E C J , TAN S S , et al . Methodology for predicting explosion risk around underground coal mine openings towards developing exclusion zones [C ] // Proceedings of the 2022 Resource Operators Conference . Wollongong,Australia : University of Wollongong Press , 2022 .

DENNIS A A , PANNELL J J , SMYL D J , et al . Prediction of blast loading in an internal environment using artificial neural networks [J ] . International Journal of Protective Structures , 2021 , 12 ( 3 ): 287 - 314 .

Views

117

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of Blended-wing-body Underwater Gliders Based on Dual-layer Multi-objective Optimization

Thickness Equivalence Model Based on Serial Neural Network for Explosion Resistance of Radome

Nonlinear Sliding Mode Control Based on Neural Network Compensation for Tank All-electric Bidirectional Stabilizers

Dynamic Response of UHPFRC Beams with Different Strengths under Blast Loading

The Damage Effects of Ultra-high Performance Fibre Reinforced Concrete Slabs under Close-range Blast Loading

Related Author

Wenxin WANG

Peng WANG

Huachao DONG

Yichen JIANG

Jinglu LI

Changfa CHEN

Jun’an WU

Rui GUO

Related Institution

School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology

School of Mechanical Engineering, Nanjing University of Science and Technology

National Key Laboratory of Transient Physics, Nanjing University of Science and Technology

Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314019, Zhejiamg.

Postal code：100089
Tel：010-68963060/68962718 Fax：010-68963025 Email：bgxb@cos.org.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备05059581号-4 京公网安备11010802024360号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：44524 Visits Today：504

⁰