On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Reliability evaluation of aeroengine rotor systems is often characterized by multiple
correlated frail sites and multiple coupled failure modes, leading to the traditional integral …

Natural gas pipeline systems are commonly designed under the assumption of constant
supply and demand flow conditions. This is while gas flows are transient because of the …

Kirigami-inspired designs hold great potential for the development of functional materials
and devices, but predicting the morphological configuration of these structures under …

Operating high-speed trains imposes excessive vibrations to bridges raising concerns about
their safety. In this context, it was shown that some conventional design methods such as …

In the recent decade, the reliability analysis of a stochastic system coupled with the
uncertainty related to the system's parameter has attracted much attention. Probability …

The well-known BUS algorithm (ie, Bayesian Updating with Structural reliability) transforms
Bayesian updating problems into structural reliability to address challenges of updating with …

Ideally, the monitoring data collected by the Structural health monitoring (SHM) system
should purely reflect the structure status. However, sensors deployed in the field can be very …

Abstract Machine learning (ML) models are gaining popularity in structural health monitoring
(SHM) because of their ability to learn the complex relationship between damage and …

In this paper, we develop a generic physics-informed neural network (PINN)-based
framework to assess the reliability of multi-state systems (MSSs). The proposed framework …