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

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 with 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.