Digital intelligence simulation model and application of urban building explosion power field and damage effect
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摘要: 建筑外爆威力场的准确预测对于工程结构毁伤评估和抗爆设计至关重要。传统经验公式由于未能充分考虑环境因素的复杂性，其预测精度往往受到限制；数值仿真能提供较为精准的超压荷载参数，但在处理大规模城市场景时效率低下，难以满足快速毁伤评估的需求。针对这一难题，本文创新性构建了一种基于图神经网络（GNN）的爆炸威力场预测模型，旨在直接利用建筑的几何特征，实现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快速精准预测。通过与数值仿真结果的对比验证，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秒，与数值仿真相比速度提升3至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。本文提出的GNN模型为爆炸场景下城市建筑爆炸威力场快速精确预测提供了一个全新方法，能极大提升超大规模复杂场景工程建筑爆炸毁伤评估和抗爆设计能力，具有重大工程价值。
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Abstract: Accurate prediction of building explosion power field is very important for damage assessment and anti-explosion design of engineering structures. The prediction accuracy of traditional empirical formulas is often limited due to the failure to fully consider the complexity of environmental factors. Numerical simulation can provide more accurate overpressure load parameters, but it is inefficient in dealing with large-scale urban scenes and difficult to meet the needs of rapid damage assessment. In order to solve this problem, this paper innovatively constructs a prediction model of explosion power field based on graph neural network ( GNN ), which aims to directly use the geometric characteristics of buildings to achieve rapid and accurate prediction of three-dimensional physical fields such as explosion peak overpressure, peak impulse and shock wave arrival time on their surfaces. By comparing with the numerical simulation results, the model shows excellent prediction performance : the mean square error of the prediction of the surface overpressure parameters of the single building with different geometric structures is0.97 % ; The average prediction error of the surface overpressure parameters of complex geometric buildings and building communities is 3.17 %. When applied to actual urban areas, the average prediction error is 1.29 % ; the single prediction of the physical field takes no more than 0.6 seconds, which is 3 to 4 orders of magnitude faster than the numerical simulation. The high-precision prediction based on the model can not only reconstruct the overpressure time history curve at any position on the building surface, but also accurately evaluate the damage degree of the structure. The GNN model proposed in this paper provides a new method for rapid and accurate prediction of the explosion power field of urban buildings in explosion scenarios, which can greatly improve the explosion damage assessment and anti-explosion design capabilities of ultra-large-scale complex scene engineering buildings, and has great engineering value. -
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