Combining transfer learning and statistical measures to predict performance of composite materials with limited data

Abstract

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.

Topics & Concepts