Machine learning in coastal bridge hydrodynamics: a state-of-the-art review

G Xu, C Ji, Y Xu, E Yu, Z Cao, Q Wu, P Lin… - Applied Ocean …, 2023 - Elsevier
Coastal bridges are vulnerable to complicated hydrodynamics induced by hostile natural
hazards, relevant research is thus required to ensure the safe operation of these critical …

Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: a new correlation based on mixture ratio

VV Wanatasanappan, PK Kanti, P Sharma… - Journal of Molecular …, 2023 - Elsevier
The present study is a pure experimental investigation of the viscosity and rheological
properties of the Al 2 O 3-Fe 2 O 3 hybrid nanofluid and the development of a new …

Machine learning applications for electrospun nanofibers: a review

B Subeshan, A Atayo, E Asmatulu - Journal of Materials Science, 2024 - Springer
Electrospun nanofibers have gained prominence as a versatile material, with applications
spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles …

Parallel adaptive Bayesian quadrature for rare event estimation

C Dang, P Wei, MGR Faes, MA Valdebenito… - Reliability Engineering & …, 2022 - Elsevier
Various numerical methods have been extensively studied and used for reliability analysis
over the past several decades. However, how to understand the effect of numerical …

An efficient and versatile Kriging-based active learning method for structural reliability analysis

J Wang, G Xu, P Yuan, Y Li, A Kareem - Reliability Engineering & System …, 2024 - Elsevier
In structural reliability analysis, the development of an efficient and versatile active learning
method applicable to problems of varying complexities remains a challenging task. The …

Biomethane production from the mixture of sugarcane vinasse, solid waste and spent tea waste: a bayesian approach for hyperparameter optimization for Gaussian …

M Alruqi, P Sharma - Fermentation, 2023 - mdpi.com
In this work, sugarcane vinasse combined with organic waste (food and wasted tea) was
demonstrated to be an excellent source of biomethane synthesis from carbon-rich biowaste …

Efficient reliability analysis using prediction-oriented active sparse polynomial chaos expansion

J Zhang, W Gong, X Yue, M Shi, L Chen - Reliability Engineering & System …, 2022 - Elsevier
In this paper, a prediction-oriented active sparse polynomial chaos expansion (PAS-PCE) is
proposed for reliability analysis. Instead of leveraging on additional techniques to reduce the …

Unified fatigue life modelling and uncertainty estimation of Ni-based superalloy family with a supervised machine learning approach

L Tan, XG Yang, DQ Shi, WQ Hao, YS Fan - Engineering Fracture …, 2022 - Elsevier
Abstract Machine learning (ML) approaches, especially the supervised learning methods,
show enormous advantages in fatigue investigation, whereas few works follow the interest in …

Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach

KV Sharma, PHVST Sai, P Sharma, PK Kanti… - … Applications of Artificial …, 2023 - Elsevier
Ceramic nanoparticles have become increasingly popular owing to their wide range of
engineering applications in the industry. Silica is one of the most promising nanomaterials …

[HTML][HTML] A new active-learning estimation method for the failure probability of structural reliability based on Kriging model and simple penalty function

Y Wang, H Pan, Y Shi, R Wang, P Wang - Computer Methods in Applied …, 2023 - Elsevier
Since the Kriging model can provide the mean value of the performance function at a
sample point and the corresponding variance among the various surrogate models, many …