城市建筑外爆威力场与毁伤效应数智仿真模型及应用

彭江舟; 潘刘娟; 高光发; 王祉乔; 胡杰; 吴威涛; 王明洋; 何勇

doi:10.11883/bzycj-2024-0471

城市建筑外爆威力场与毁伤效应数智仿真模型及应用

doi: 10.11883/bzycj-2024-0471

1.
南京理工大学机械工程学院，江苏南京 210094
2.
南京理工大学安全科学与工程学院，江苏南京 210094
3.
陆军工程大学爆炸冲击与防灾减灾国家重点实验室，江苏南京 210007

基金项目: 国家重点研发计划项目课题（Grant No. 2021YFC3100705）

详细信息

作者简介:
彭江舟（1996－　），男，博士，博士后，pengjz@njust.edu.cn

通讯作者:
何　勇（1964－　），男，博士，教授，he1964@mail.njust.edu.cn

中图分类号: O389
计量
- 文章访问数: 268
- HTML全文浏览量: 37
- PDF下载量: 86
- 被引次数: 0
出版历程
- 收稿日期: 2024-12-02
- 修回日期: 2025-03-19
- 网络出版日期: 2025-03-26

A digital intelligence simulation model for explosion power field and urban building damage effect and its application

1.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
2.
School of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
3.
State Key Laboratory of Explosion & Impact and Disaster Prevention & Mitigation, Army Engineering University of PLA, Nanjing 210007, Jiangsu, China

摘要

摘要: 为准确预测建筑外爆威力场，解决传统经验公式中未能充分考虑环境因素的复杂性而导致的精度受限、数值仿真在处理大规模城市场景时效率低下的难题，构建了一种基于图神经网络（graph neural network, GNN）的爆炸威力场预测模型，直接利用建筑的几何特征，对其表面的爆炸峰值超压、峰值冲量及冲击波到达时间等三维物理场的进行预测。与数值仿真结果的对比验证表明，本文模型展现出了卓越的预测性能：对不同几何结构的单体建筑表面超压参数的预测均方误差为0.97%；对复杂几何建筑、建筑群落建筑表面超压参数的平均预测误差为3.17%；当应用于实际城市区域时，平均预测误差为1.29%；物理场单次预测耗时不超过0.6 s，与数值仿真相比速度提升3~4个数量级。基于模型的高精度预测，不仅可以重构建筑表面任意位置的超压时程曲线，还能准确评估结构的毁伤程度。
- 爆炸威力场 /
- 毁伤评估 /
- 爆炸冲击 /
- 数智仿真 /
- 图神经网络
Abstract: To accurately predict the explosion power fields in buildings, solving the failure of traditional empirical formulas often failing to account for complex environmental factor due to their inability to account for complex environmental factors, and that of numerical simulations inefficient for large-scale urban scenarios and do not meet the needs of rapid damage assessment. Addressing this challenge, an innovative prediction model for explosion power fields based on Graph Neural Networks (GNN) was constructed using an end-to-end strategy. This model enabled rapid and precise forecasting of three-dimensional physical fields, including peak overpressure, peak impulse, and shock-wave arrival times on building surfaces. Compared to numerical simulations, the proposed GNN model demonstrated excellent predictive performance: it achieved a mean square error of 0.97% for predicting surface overpressure parameters of single buildings with varying geometries, and an average prediction error of 3.17% for complex geometric buildings and building communities. When applied to real-world urban settings, the model maintains an average prediction error of 1.29%, completing individual physical field predictions in under 0.6 seconds—three to four orders of magnitude faster than numerical simulations. Furthermore, the model's high-precision predictions allow for the reconstruction of overpressure time history curves at any building surface location and the accurate assessment of structural damage. The proposed GNN model offers a novel approach for rapidly and accurately predicting explosion power fields in urban buildings during blast events. This advancement significantly enhances the capabilities for explosion damage assessment and anti-explosion design in ultra-large-scale complex engineering scenarios, providing substantial engineering value.
- explosion power fields /
- damage assessment /
- explosion shock /
- data-driven intelligent simulation model /
- graph neural networks

HTML全文

图 1 方法流程图

Figure 1. Methodology flow chart

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图 2 实验与仿真的爆炸场景

Figure 2. Experimental and simulated explosion scenarios

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图 3 仿真方法的有效性验证

Figure 3. Validation of simulation methods

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图 4 建筑外爆数值仿真

Figure 4. Numerical simulation of building implosion

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图 5 200 kg当量爆炸在城市区域的冲击波耗散与传播特性

Figure 5. Dissipation and propagation characteristics of shock waves from a 200kg TNT equivalent explosion in urban areas.

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图 6 训练测试集空间分布示例

Figure 6. Spatial distribution example of the training test sets

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图 7 爆炸超压载荷参数预测模型网络架构

Figure 7. Network architecture of the blast overpressure load parameter prediction model

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图 8 训练损失收敛情况（1 epoch=3200 steps）

Figure 8. Training loss convergence (1 epoch=3200 steps)

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图 9 单体建筑测试集R²分布

Figure 9. R² distribution on the single building test sets

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图 10 单体建筑样本预测结果

Figure 10. Prediction results of single building samples

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图 11 一随机测试数据y=37 m截面处预测结果

Figure 11. Prediction results at y=37 m cross-section of a random test data

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图 12 复杂几何建筑与建筑群落样本预测结果

Figure 12. Prediction results of multiple buildings and complex geometric building samples

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图 13 复杂建筑构型一随机数据 x=55 m截面处预测结果

Figure 13. Prediction results at x=55 m cross-section of a random test data in complex geometric building samples

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图 14 建筑群落构型一随机数据 x=74 m截面处预测结果

Figure 14. Prediction results at x=74 m cross-section of a random test data in multiple buildings samples

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图 15 城市数字孪生建模和爆炸事件分布

Figure 15. Modeling of urban digital twins and distribution of explosion cases

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图 16 城市区域爆炸预测结果

Figure 16. Prediction results of explosion in urban areas

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图 17 超压时程曲线重构

Figure 17. Reconstruction of the overpressure time history curve

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图 18 峰值压力-峰值冲量曲线

Figure 18. Curves of peak pressure vs. peak impulse

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图 19 毁伤等级划分结果

Figure 19. Damage classification results

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表 1 GNN和OpenFOAM计算硬件配置对比表格

Table 1. 1 Computing Hardware Configuration Comparison Table of GNN and OpenFOAM

计算方法	操作系统	开发语言	CPU	GPU
GNN	Ubuntu 20.04	Python	Intel Xeon Gold 5317	GeForce RTX 4090ke
OpenFOAM	Ubuntu 20.04	C++	AMD EPYC 7H12	无显卡

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表 2 复杂几何建筑与建筑群落测试集模型预测性能

Table 2. Predictive performance of GNN models on the test sets of the complex geometric building and multiple buildings

建筑类型	ε_RSE/%			计算时间/s
建筑类型	峰值超压	峰值冲量	到时	OpenFOM	GNN
复杂建筑—十二边形	1.15	2.34	0.85	991	0.15
复杂建筑—扇形	1.51	3.68	1.33	857	0.14
复杂建筑—十字形	1.16	6.69	2.74	984	0.35
建筑群落—2栋	1.95	8.16	2.04	991	0.27
建筑群落—3栋	1.67	7.57	3.07	1450	0.36
建筑群落—4栋	2.61	6.52	1.99	2045	0.51

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表 3 城市区域爆炸事件GNN模型预测性能

Table 3. Predictive performance of the GNN model for the blasts in urban areas

爆炸事件	ε_RSE/%			计算时间/s
爆炸事件	峰值超压	峰值冲量	到时	OpenFOAM	GNN
1	2.74	6.06	0.89	1143	0.37
2	0.92	5.10	0.85	875	0.34
3	1.19	4.72	1.22	984	0.20
4	0.74	5.32	1.48	928	0.21
5	0.98	4.24	2.63	961	0.13
6	1.36	3.01	0.80	1105	0.23

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