State-of-the-art AI-based computational analysis in civil engineering

C Wang, L Song, Z Yuan, J Fan - Journal of Industrial Information …, 2023 - Elsevier
With the informatization of the building and infrastructure industry, conventional analysis
methods are gradually proving inadequate in meeting the demands of the new era, such as …

[HTML][HTML] From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems

T Wang, M Noori, WA Altabey, Z Wu, R Ghiasi… - … Systems and Signal …, 2023 - Elsevier
Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems.
The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous …

Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

H Hu, L Qi, X Chao - Thin-Walled Structures, 2024 - Elsevier
For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading

M Bartošák - International Journal of Fatigue, 2022 - Elsevier
In this article, machine learning is used to predict lifetime under isothermal low-cycle fatigue
and thermo-mechanical fatigue loading, both of which represent the most complex loadings …

Determination of material parameters in constitutive models using adaptive neural network machine learning

J Wang, B Zhu, CY Hui, AT Zehnder - Journal of the Mechanics and Physics …, 2023 - Elsevier
A challenge in the constitutive modeling of time dependent, non-linear solids is the
identification of potentially large numbers of material parameters. Here we present an …

[HTML][HTML] Machine learning of evolving physics-based material models for multiscale solid mechanics

IBCM Rocha, P Kerfriden, FP Van Der Meer - Mechanics of Materials, 2023 - Elsevier
In this work we present a hybrid physics-based and data-driven learning approach to
construct surrogate models for concurrent multiscale simulations of complex material …

Physics-informed few-shot deep learning for elastoplastic constitutive relationships

C Wang, Y He, H Lu, J Nie, J Fan - Engineering Applications of Artificial …, 2023 - Elsevier
Elastoplastic modeling is essential for accurately predicting material behavior in various
engineering applications. However, existing approaches to developing intelligent models for …

Online monitoring model of micro-milling force incorporating tool wear prediction process

P Ding, X Huang, C Zhao, H Liu, X Zhang - Expert Systems with …, 2023 - Elsevier
In modern manufacturing, micro-milling technology plays an essential role in manufacturing
high-precision and complex micro-size parts. Exploring the changing rule of time-varying …

Fatigue behaviour of plain and reinforced concrete: a systematic review

RL Riyar, S Bhowmik - Theoretical and Applied Fracture Mechanics, 2023 - Elsevier
Damage tolerance design approach such as the fatigue crack propagation approach
provides a more realistic solution for fatigue problems in concrete to overcome the …

[HTML][HTML] Micromechanics-based deep-learning for composites: Challenges and future perspectives

M Mirkhalaf, I Rocha - European Journal of Mechanics-A/Solids, 2024 - Elsevier
During the last few decades, industries such as aerospace and wind energy (among others)
have been remarkably influenced by the introduction of high-performance composites. One …