Predicting the performance of composite materials is crucial for their application in civil infrastructure, yet limited experimental data often hinder the development of accurate and generalizable models. This study introduces a deep neural network (DNN) approach that combines summarizing statistics (SS) and transfer learning (TL)—termed the SSTL‐DNN approach—to address data scarcity in modeling composite materials. The computational novelty lies in the SS method's ability to extract comprehensive information from limited datasets by converting complex constitutive laws into concise statistical representations, thereby enabling efficient and effective model training. Simultaneously, the TL method enhances computational efficiency by leveraging knowledge from related tasks with abundant data to improve learning in the target task with scarce data. This combination not only reduces dependency on large datasets but also significantly improves model generalization. The proposed SSTL‐DNN approach is validated through two case studies: fiber‐reinforced polymer confined concrete and engineered cementitious composites. In both case studies, the SSTL‐DNN model reduces the required dataset size by up to 75% and decreases the validation error by 39%, compared to traditional deep learning models. These results demonstrate that the SSTL‐DNN approach not only overcomes data scarcity but also provides accurate predictions and generalization to unseen data, offering a practical solution for modeling composite materials with limited data.

中文翻译：

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结合迁移学习和统计测量来预测数据有限的复合材料的性能


预测复合材料的性能对于其在民用基础设施中的应用至关重要，但有限的实验数据往往阻碍了准确和可推广模型的开发。本研究介绍了一种深度神经网络 （DNN） 方法，该方法结合了汇总统计 （SS） 和迁移学习 （TL），称为 SSTL-DNN 方法，以解决复合材料建模中的数据稀缺问题。计算新颖性在于 SS 方法能够通过将复杂的本构定律转换为简洁的统计表示，从有限的数据集中提取综合信息，从而实现高效的模型训练。同时，TL 方法通过利用具有丰富数据的相关任务中的知识来提高计算效率，从而改善在数据稀缺的目标任务中的学习。这种组合不仅减少了对大型数据集的依赖，还显著提高了模型泛化能力。所提出的 SSTL-DNN 方法通过两个案例研究进行了验证：纤维增强聚合物约束混凝土和工程水泥基复合材料。在这两个案例研究中，与传统深度学习模型相比，SSTL-DNN 模型将所需的数据集大小减少了 75%，并将验证误差减少了 39%。这些结果表明，SSTL-DNN 方法不仅克服了数据稀缺问题，而且还提供了对看不见的数据的准确预测和泛化，为使用有限数据对复合材料进行建模提供了实用的解决方案。