The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
Machine learning (ML) has evolved rapidly over recent years with the promise to
substantially alter and enhance the role of data science in a variety of disciplines. Compared …
substantially alter and enhance the role of data science in a variety of disciplines. Compared …
[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 …
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
S Chatterjee, S Sarkar, S Hore, N Dey… - Neural Computing and …, 2017 - Springer
Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse
suddenly. All attempts are directed to avoid structural failure as it leads to human life danger …
suddenly. All attempts are directed to avoid structural failure as it leads to human life danger …
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 …
Neural network based prediction schemes of the non-linear seismic response of 3D buildings
ND Lagaros, M Papadrakakis - Advances in Engineering Software, 2012 - Elsevier
Since early 1980s new families of computational methods, termed as soft computing (SC)
methods, have been proposed. SC methods are based on heuristic approaches rather than …
methods, have been proposed. SC methods are based on heuristic approaches rather than …
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 …
Deep learning models for time-history prediction of vehicle-induced bridge responses: A comparative study
Time-history responses of the bridge induced by the moving vehicle provide crucial
information for bridge design, operation, maintenance, etc. As inspired by this, this work …
information for bridge design, operation, maintenance, etc. As inspired by this, this work …
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 …