基于图神经网络的建筑侵爆毁伤数智仿真模型  

Digital-Intelligent Simulation Model for Building Penetration-Explosion Damage Based on Graph Neural Network

作　　者：吴威涛 谢文 王祉乔 何勇[1] 熊自明 李文宇 WU Weitao;XIE Wen;WANG Zhiqiao;HE Yong;XIONG Ziming;LI Wenyu(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210049,China;Nanjing Campus of Army Academy of Artillery and Air Defense,Nanjing 211132,China;College of National Defense Engineering,Army Engineering University of PLA,Nanjing 210007,China;School of Safety Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院,江苏南京210049 [2]陆军炮兵防空兵学院南京校区,江苏南京211132 [3]陆军工程大学国防工程学院,江苏南京210007 [4]南京理工大学安全科学与工程学院,江苏南京210049

出　　处：《陆军工程大学学报》2025年第2期71-79,共9页Journal of Army Engineering University of PLA

基　　金：国家自然科学基金(52278419,U2241285);江苏省自然科学基金(BK20241439)。

摘　　要：针对现阶段钢筋混凝土建筑物侵彻爆炸精细毁伤评估主要依赖数值模拟以及试验数据分析,且这两种方法均存在耗时长、经济代价高的问题,提出了一种全新的侵爆毁伤数智预测模型,用于侵彻-爆炸全过程动力学模拟。模型基于图神经网络以及多任务学习范式,能够实现不同侵彻位置、爆炸当量条件下建筑毁伤形貌与节点失效的快速、精确预测,单次侵爆预测耗时仅0.2 s。其中,侵彻阶段结构响应场预测误差低于2.9%,节点失效状态预测准确率高于99.5%,爆炸阶段结构响应场预测误差低于6.5%,失效预测准确率高于98.8%,百次毁伤评估耗时仅需30 s。所提数智预测模型表现出优异的泛化能力以及高效精确的预测能力,为建筑结构损伤评估、安全防护设计等领域提供了一种全新工具。At present,the refined damage assessment of reinforced concrete structures subjected to penetration explosion mainly relies on numerical simulations and experimental data analysis,both of which suffer from the drawbacks of time-consuming processes and high economic costs.To address these issues,a novel digital-intelligent prediction model for penetration-explosion damage is proposed to simulate the entire dynamics of the penetration-explosion process.Based on the graph neural network and a multi-task learning paradigm,this model can achieve rapid and accurate predictions of building damage morphology and node failures under various penetration positions and explosion yields,with a single penetration-explosion prediction taking only 0.2 seconds.Specifically,during the penetration phase,the structural response field prediction error is less than 2.9%,and the node failure prediction accuracy exceeds 99.5%;during the explosion phase,the structural response field prediction error is lower than 6.5%,and the failure prediction accuracy surpasses 98.8%.The evaluation of 100 penetration-explosion damage scenarios takes only 30 s.The proposed digital-intelligent prediction model demonstrates excellent generalization performance and highly efficient,accurate prediction capability,thus providing a new tool for building structure damage assessment and safety protection design.

关 键 词：钢筋混凝土建筑 侵爆毁伤评估 图神经网络 多任务学习 

分 类 号：TU352.1[建筑科学—结构工程]