Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

有效地预测使用阶段的故障数据对于合理制定可靠性计划以及开展可靠性维护活动等具有重要
的指导意义. 从复杂系统的历史故障数据出发, 提出了一种基于长短期记忆(LSTM) …

In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Developing predictive models plays an important role …

Traffic flow prediction is an essential component of the intelligent transportation
management system. This study applies gated recurrent neural network to predict urban …

The nonlinear regression estimation issues are solved by successful application of a novel
neural network technique termed as support vector machines (SVMs). Evaluation of …

To enhance computational efficiency in reliability analysis, metamodeling has been widely
adopted for reliability assessment. This work develops an efficient reliability method which …

Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …

This paper proposes a uniform design (UD)-based Gaussian process regression (GPR)
method for rapid damage assessment and fragility estimates of bridges. The core idea of the …

Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics
and health management (PHM) by reason of the increasing safety requirements of industrial …