[HTML][HTML] A review of physics-based machine learning in civil engineering
The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …
opportunities in all the sectors. ML is a significant tool that can be applied across many …
Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
H Wang, B Li, J Gong, FZ Xuan - Engineering Fracture Mechanics, 2023 - Elsevier
Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …
mechanical structures. Although data-driven approaches have been proven effective in …
The potency of defects on fatigue of additively manufactured metals
Given their preponderance and propensity to initiate fatigue cracks, understanding the effect
of processing defects on fatigue life is a significant step towards the wider application of …
of processing defects on fatigue life is a significant step towards the wider application of …
Fatigue modeling using neural networks: A comprehensive review
Neural network (NN) models have significantly impacted fatigue‐related engineering
communities and are expected to increase rapidly due to the recent advancements in …
communities and are expected to increase rapidly due to the recent advancements in …
[HTML][HTML] Physics-informed machine learning: a comprehensive review on applications in anomaly detection and condition monitoring
Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …
various engineering systems. Traditional methods for condition monitoring rely on physics …
A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network
Abstract Machine learning has drawn growing attention from the areas of fatigue, fracture,
and structural integrity. However, most current studies are fully data-driven and may …
and structural integrity. However, most current studies are fully data-driven and may …
A review of machine learning methods applied to structural dynamics and vibroacoustic
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …
applied sciences, having encountered many applications in Structural Dynamics and …
A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities
H Li, Z Zhang, T Li, X Si - Mechanical Systems and Signal Processing, 2024 - Elsevier
Remaining useful life (RUL) prediction, known as 'prognostics', has long been recognized as
one of the key technologies in prognostics and health management (PHM) to maintain the …
one of the key technologies in prognostics and health management (PHM) to maintain the …
Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materials
Additive manufacturing (AM) has attracted many attentions because of its design freedom
and rapid manufacturing; however, it is still limited in actual application due to the existing …
and rapid manufacturing; however, it is still limited in actual application due to the existing …
A novel deep learning approach of multiaxial fatigue life-prediction with a self-attention mechanism characterizing the effects of loading history and varying …
A novel deep learning approach is established in this work to directly model the highly
nonlinear map** between the complex loading conditions (input) and the multiaxial …
nonlinear map** between the complex loading conditions (input) and the multiaxial …