Due to the absence of adequate control at different stages of complex manufacturing process, material and geometric properties of composite structures are often uncertain. For a secure and safe design, tracking the impact of these uncertainties on the structural responses is of utmost significance. Composite materials, commonly adopted in various modern aerospace, marine, automobile and civil structures, are often susceptible to low-velocity impact caused by various external agents. Here, along with a critical review, we present machine learning based probabilistic and non-probabilistic (fuzzy) low–velocity impact analyses of composite laminates including a detailed deterministic characterization to systematically investigate the consequences of source- uncertainty. While probabilistic analysis can be performed only when complete statistical description about the input variables are available, the non-probabilistic analysis can be executed even in the presence of incomplete statistical input descriptions with sparse data. In this study, the stochastic effects of stacking sequence, twist angle, oblique impact, plate thickness, velocity of impactor and density of impactor are investigated on the crucial impact response parameters such as contact force, plate displacement, and impactor displacement. For efficient and accurate computation, a hybrid polynomial chaos based Kriging (PC-Kriging) approach is coupled with in-house finite element codes for uncertainty propagation in both the probabilistic and non- probabilistic analyses. The essence of this paper is a critical review on the hybrid machine learning algorithms followed by detailed numerical investigation in the probabilistic and non-probabilistic regimes to access the performance of such hybrid algorithms in comparison to individual algorithms from the viewpoint of accuracy and computational efficiency.

由于在复杂的制造过程的不同阶段缺乏适当的控制，因此复合结构的材料和几何特性通常不确定。对于安全设计而言，跟踪这些不确定性对结构响应的影响至关重要。在各种现代航空航天，船舶，汽车和民用建筑中通常采用的复合材料通常容易受到各种外部因素引起的低速冲击的影响。在这里，与批判性评论一起，我们介绍了基于机器学习的复合材料层压板的概率和非概率（模糊）低速冲击分析，包括详细的确定性表征，以系统地调查源不确定性的后果。虽然仅当可获得有关输入变量的完整统计描述时才可以执行概率分析，但即使在存在稀疏数据的不完整统计输入描述的情况下，也可以执行非概率分析。在这项研究中，研究了堆垛顺序，扭曲角，倾斜冲击，板厚，冲击器速度和冲击器密度对关键冲击响应参数（如接触力，板位移和冲击器位移）的随机影响。为了高效，准确地进行计算，将基于混合多项式混沌的Kriging（PC-Kriging）方法与内部有限元代码相结合，以便在概率分析和非概率分析中传播不确定性。