The present study proposes a two-stage Bi-Fidelity Deep Neural Network surrogate model to
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …

Due to the thermal inertia of building envelope and random uncertainty of occupant
behaviors, real-time and accurate forecasting for building cooling and heating loads is not …

A method based on a Bi-fidelity Kriging model is proposed for structural reliability analysis. It
is based on adding low-fidelity data samples to the model to predict high-fidelity values, thus …

The experimental observations of fatigue life of metals always exhibit uncertainty even under
the same settings. How to effectively capture the uncertainty when predicting fatigue life of …

Lately, several machine learning (ML) techniques are emerging as alternative and efficient
ways to predict how component properties influence the properties of the final mixture. In the …

The blade is the component responsible for capturing energy in the Savonius hydraulic
turbine, and a good blade shape can extract more energy from the flow field. The shape of …

Aeroengine blisks operate in a harsh working environment and are prone to low cycle
fatigue (LCF) failure. The probabilistic LCF life prediction considering multiple uncertainties …

Morphing structures have received growing interest in aerospace structures and wind
turbines due to their rapid shape-changing ability in response to the change in operating …

The interaction between soils and geosynthetics plays an important role in the applications
of these materials for reinforcement in geotechnical engineering. The complexities of soil …

The macroscopic mechanical properties of biological composite materials, such as strength
and fracture toughness, are determined by both their constituents and microstructure …