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1.太原理工大学 航空航天学院,山西 太原 030024
2.材料强度与结构冲击山西省重点实验室,山西 太原 030024
3.山西省力学基础学科研究中心,山西 太原 030024
4.力学国家级实验教学示范中心,山西 太原 030024
Received:24 November 2025,
Online First:23 July 2026,
Published:31 July 2026
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朱星明,李志强,郑少秋等.爆炸载荷下透明夹层结构的动态响应与结构轻量化设计[J].兵工学报,2026,47(07):55-70.
ZHU Xingming,LI Zhiqiang,ZHENG Shaoqiu,et al.Lightweight Design of Transparent Laminated Structures and Its Dynamic Response under Blast Loads[J].ACTA ARMAMENTARII,2026,47(07):55-70.
朱星明,李志强,郑少秋等.爆炸载荷下透明夹层结构的动态响应与结构轻量化设计[J].兵工学报,2026,47(07):55-70. DOI: 10.12382/bgxb.2025.1027.
ZHU Xingming,LI Zhiqiang,ZHENG Shaoqiu,et al.Lightweight Design of Transparent Laminated Structures and Its Dynamic Response under Blast Loads[J].ACTA ARMAMENTARII,2026,47(07):55-70. DOI: 10.12382/bgxb.2025.1027.
透明夹层结构广泛应用于现代建筑和军事防护领域,研究其在爆炸载荷作用下的动态响应机理对于指导工程设计与提升结构安全性具有重要意义。采用试验与数值模拟相结合的方法,系统分析不同爆炸载荷下透明夹层结构的破坏模式及各功能层厚度对抗爆性能的影响。通过试验与数值模拟结果对比,验证有限元模型的准确性。开展大量数值模拟,拟合得到爆炸距离、炸药量及各功能层厚度与结构最大挠度之间的无量纲关系曲线。基于数值模拟结果建立超压-冲量(P-I)曲线,界定结构在不同压力与冲量条件下的损伤界限,为抗爆结构设计提供了明确的指导依据。利用多种机器学习方法建立中心挠度预测模型,并筛选出预测性能最优的模型,结合遗传算法,以最小质量和最小总厚度为目标进行夹层结构的轻量化设计。研究结果表明:1)透明夹层结构能够承受较大的爆炸载荷并保持稳定性,其损伤程度随爆炸载荷强度的增加而加剧,表现出3种典型破坏模式;2)各功能层厚度对整体抗爆性能具有显著影响。在其他层厚度保持恒定的条件下,增加陶瓷层、无机玻璃层或聚碳酸酯(Polycarbonate,PC)层厚度,会导致陶瓷层和无机玻璃层损伤增加但使PC层的变形减小,从而整体提高结构的抗爆性能;3)基于数值模拟所得的P-I曲线能够准确预测结构在不同压力和冲量条件下的损伤模式及安全阈值,为工程设计提供直观有效的设计依据;4)机器学习模型与遗传算法的结合,有效实现了透明夹层结构的轻量化设计,在保证抗爆性能的同时显著降低了结构的质量与厚度。
The transparent laminated structures are widely used in modern architecture and military protection. Investigating their dynamic response mechanisms under blast loads is of great significance for engineering design and the improvement of structural safety. A combined experimental and numerical approach is employed to systematically analyze the failure modes of transparent laminated structures under different blast loads, as well as the effects of the thicknesses of the functional layers on blast resistance. The accuracy of the finite element model is validated by comparing the experimental results with the numerically simulated results. Extensive numerical simulations were conducted to establish the dimensionless relationships among maximum structural deflection, and blast distance, explosive charge, and functional-layer thickness. Based on the numerical results, the pressure–impulse (P-I) curves are established to define the damage boundaries of the structure under different pressure and impulse conditions, thereby providing clear guidance for the design of blast-resistant structure. In addition, the prediction models for the central deflection are developed using multiple machine-learning methods are used to develop, and a model with the best predictive performance is selected from among them. The lightweight design of the laminated structure is achieved by taking the minimum mass and total thickness as the optimal objectives. The results show that the transparent laminated structures can withstand relatively large blast loads while maintaining the structural stability, and their damage severity increases with blast-load intensity, exhibiting three typical failure modes. The thickness of each functional layer has a significant effect on the overall blast resistance. When the thicknesses of the other layers are kept constant, increasing the thickness of the ceramic layer, inorganic glass layer, or polycarbonate (PC) layer leads to more severe damage to the ceramic and inorganic glass layers, but reduces the deformation of the PC layer, thereby improving the overall blast resistance of the structure.The damage modes and safety thresholds of the structure under different pressure and impulse conditions can be accurately predicted from the simulated P-I curves, thus providing an intuitive and effective basis for engineering design. The combination of machine-learning models and a genetic algorithm effectively realizes the lightweight design of transparent laminated structures, significantly reducing structural mass and thickness while ensuring blast resistance.
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