Sajad Saraygord Afshari

TL;DR: A review of the use of ML models in structural reliability analysis can be found in this article, which includes the most common types of ML methods used in SRA, including artificial neural networks (ANN), support vector machines (SVM), Bayesian methods and Kriging estimation with active learning perspective.

TL;DR: In this article, a new multiscale domain-adversarial neural network is proposed to extract representative features from the data collected under different working conditions by introducing the maximum mean discrepancy regularizer and the Laplace regularizer, which can enhance features' discriminant ability for incipient fault and exploit overall degradation information simultaneously.

TL;DR: In this article, the active vibration control of a cantilevered flexible beam structure equipped with bonded piezoelectric sensor/actuators is investigated, and two different approaches are subsequently used for simultaneously integrated optimization of the controller and observer parameters.

TL;DR: In this article, a hybrid model is proposed for wear prediction of a single revolute joint that may experience different lubrication conditions, such as full-film, boundary and dry contact.

TL;DR: Wang et al. as mentioned in this paper proposed a deep domain adaptation neural network for joint adversarial training at feature level and model level simultaneously, and then a domain-invariant feature representation can be extracted from the data of different working conditions, and an online detection model can then be constructed.