Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering
The fields of machining learning and artificial intelligence are rapidly expanding, impacting
nearly every technological aspect of society. Many thousands of published manuscripts …
nearly every technological aspect of society. Many thousands of published manuscripts …
[HTML][HTML] From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems
Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems.
The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous …
The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous …
Seismic response prediction of structures based on Runge-Kutta recurrent neural network with prior knowledge
In the seismic analysis of structural systems, dynamic response prediction is an essential
problem and is significant in every stage during the structural life cycle. Conventionally …
problem and is significant in every stage during the structural life cycle. Conventionally …
Data-driven seismic response prediction of structural components
H Luo, SG Paal - Earthquake Spectra, 2022 - journals.sagepub.com
Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays
an important role in resisting the lateral earthquake loads. The lateral stiffness relates the …
an important role in resisting the lateral earthquake loads. The lateral stiffness relates the …
Probabilistic seismic response prediction of three-dimensional structures based on Bayesian convolutional neural network
Seismic response prediction is a challenging problem and is significant in every stage
during a structure's life cycle. Deep neural network has proven to be an efficient tool in the …
during a structure's life cycle. Deep neural network has proven to be an efficient tool in the …
Parameter identification and dynamic response analysis of a modified Prandtl–Ishlinskii asymmetric hysteresis model via least-mean square algorithm and particle …
Hysteresis is a nonlinear phenomenon observed in the dynamic response behavior of
numerous structural systems under high intensity cyclic or random loading, as well as in …
numerous structural systems under high intensity cyclic or random loading, as well as in …
A digital twin-based framework for multi-element seismic hybrid simulation of structures
F Mokhtari, A Imanpour - Mechanical Systems and Signal Processing, 2023 - Elsevier
This paper proposes a digital twin-based multi-element hybrid simulation (DMHS)
framework to predict the nonlinear cyclic response of structural components (digital twin), eg …
framework to predict the nonlinear cyclic response of structural components (digital twin), eg …
Machine learning-based soil–structure interaction analysis of laterally loaded piles through physics-informed neural networks
This research adopts emerging machine learning techniques to tackle the soil–structure
interaction analysis problems of laterally loaded piles through physics-informed neural …
interaction analysis problems of laterally loaded piles through physics-informed neural …
Leveraging full-field measurement from 3D digital image correlation for structural identification
Within the domain of structural health monitoring (SHM) measurement techniques have
primarily relied on discrete sensing strategies using sensors physically attached to the …
primarily relied on discrete sensing strategies using sensors physically attached to the …
Neurocomputing in civil infrastructure
JP Amezquita-Sanchez… - Scientia …, 2016 - scientiairanica.sharif.edu
This article presents a review of the recent applications of artificial neural networks (ANN) for
civil infrastructure including structural system identification, structural health monitoring …
civil infrastructure including structural system identification, structural health monitoring …