Self-constructed strategy-based reinforcement LSTM approach for fiber-reinforced polymer non-linear degradation performance analysis

The performance degradation of fiber-reinforced polymer (FRP) is a typical sequential data with a highly non-linear evolution pattern. In this study, a Self-constructed strategy-based reinforcement LSTM approach (short for SCRLA) is proposed to self-accommodate the non-linear sequential data, reduce modeling burden, and improve generalization ability. SCRLA incorporates Bayesian algorithms introducing the uncertainty of hyperparameter optimization by a probabilistic distribution of implicit objectives and self-constructed a high-dimensional, reinforced machine model that can learn and predict non-linear representations. In the case study, the datasets with different properties (one consisting of finite element analysis (FEA) results and one of real experimental (EXP) data) are selected to verify the validity of the degradation performance predictions. It is shown that the reinforced LSTM based on SCRLA is more suitable for the non-linear degradation performance analysis of FRP, especially with higher prediction accuracy for EXP data. The establishment of the approach and model provides a feasible idea and framework for the prediction of composites' sequential performance.