爆炸与冲击2026，Vol.46Issue(5)：3-17,15.DOI:10.11883/bzycj-2025-0238
基于物理信息及数据融合驱动的复杂街区爆炸荷载快速计算方法
A physics-information and data fusion-driven method for rapid prediction of blast loads in complex urban environments
摘要
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.关键词
复杂城市街区/爆炸荷载/镜像爆源法/能量密度因子/深度学习Key words
complex urban environment/explosion load/method of images/energy density factor/deep learning分类
数理科学引用本文复制引用
黄阳,罗定坤,陈素文..基于物理信息及数据融合驱动的复杂街区爆炸荷载快速计算方法[J].爆炸与冲击,2026,46(5):3-17,15.基金项目
国家自然科学基金(52578603) （52578603）
上海市科委"科技创新行动计划"(22dz1202900) （22dz1202900）
同济大学学科交叉联合攻关重点项目(2023-2-ZD-05) （2023-2-ZD-05）
