摘要:
快速准确地评估复杂街区爆炸荷载对实现高效结构抗爆设计及灾后损伤评估具有重要意义，然而传统经验公式、物理模型及数值模拟方法难以兼顾计算效率与预测精度，而现有深度学习爆炸荷载预测模型尚难以用于复杂街区场景。为实现复杂街区爆炸荷载快速准确计算，提出了一种物理信息与数据融合驱动的复杂街区爆炸荷载预测方法，其基本思想是“空间分区、逐步推理”策略，分别针对“起爆街道”和“非起爆街道”构建快速网络预测模型，并通过各街道间的边界压力协同工作。两种网络预测模型通过分别引入镜像爆源方法和信号距离场、能量密度因子融合流场关键物理特征，并分别采用3D-UNet网络、2D-UNet联合3D-UNet组成的级联网络作为架构。基于验证后的数值模拟方法生成了两种网络的目标数据，并开展了对应模型训练。模型预测性能的评估结果表明：该方法能够准确预测复杂街区压力场的时空演化过程，在起爆街道和非起爆街道中的流场预测结果较数值模拟结果的相对误差在20%以内，并有效描述了流场中指定位置的压力时程。双网络协同方法的推理耗时约为对应数值模拟方法计算时间的2%，单一时刻流场数据存储代价小于对应D3PLOT文件的0.2%，显著降低了计算与数据存储代价。研究为大型复杂街区爆炸荷载快速评估提供了新方法，可为城市建筑抗爆设计和评估提供高效决策支持。
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. This 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.
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