Uncertainty quantification of compressive after impact strength in laminated composites using improved <scp>B</scp>ayesian <scp>ResNet</scp> deep learning model

Abstract This study, for the first time, presented a novel Bayesian inference deep learning model based on a dilated convolutional ResNet framework, utilizing acoustic emission (AE) signals as inputs to quantify the uncertainty of the compression after impact (CAI) strength in composites. The output of the deep learning model included the mean CAI and the predictive aleatoric uncertainty. The epistemic uncertainty of the model was estimated by randomly sampling the input multiple times. AE signals were divided into a complete training dataset and a partially missing training dataset to investigate the impact of data insufficiency on the uncertainty of deep learning models. The results demonstrate that the deep learning model can provide highly accurate predictions of CAI performance. It is worth noting that on incomplete datasets, the epistemic uncertainty of the predictions increased significantly. To evaluate the effect of noise on the prediction of CAI strength, Gaussian noise with signal‐to‐noise ratios (SNRs) of 10, 5, and 2 dB was introduced during the prediction phase. The Bayesian posterior probability density function becomes lower and wider under the same impact energy as external noise increases. The model exhibited excellent regression performance under SNRs of 10 and 5 dB, validating the robustness of the proposed framework. As noise levels increased, the range of aleatoric uncertainty in the predictions expanded. Highlights A dilated convolution Bayesian deep learning framework for uncertainty quantification of compression after impact strength in laminated composites is proposed. The prediction and uncertainty quantification performance of the framework is evaluated. The effect of data missing and background noise on the uncertainty is investigated.