Procedures to build trust in nonlinear elastoplastic integration algorithm: solution and code verification

Yuan, Feng; Zamani, Kaveh; Yang, Han; Wang, Hexiang; Wang, Fangbo; Jeremić, Boris

doi:10.1007/s00366-019-00787-0

Cited by 6 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization methods that have been used to geotechnical and geomechanical problems are: classical (iterative) optimization method, inverse analysis, genetic algorithm, and artificial neural network. Previous studies expose the capability of the classical optimization in geotechnical and geomechanical applications to calibrate appropriately the elastoplastic model parameters (Mattsson et al, 2001;Cekerevac et al, 2006;Doherty et al, 2012, Feng et al, 2019. Classical optimization method requires the search for the minimum value of an objective function over the whole domain according to whether it evaluates Hessians, gradient, or only function values (Mattsson et al, 2001;Doherty et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Sanei,

Devloo,

Forti

et al. 2023

Preprint

View full text Add to dashboard Cite

Optimization is a branch of applied mathematics which is used extensively in almost all areas of decision-making. In particular, classical method is a kind of the optimization methods that can be applied to make decisions. The optimization method can only make a better decision if the algorithm starts with an appropriate initial data. The main purpose of this study is two-folds. First, the material parameters of multivariable plasticity models, such as Mohr-Coulomb and DiMaggio-Sandler are calibrated via the gradient based optimization method by considering the appropriate initial data from porous rock database. Moreover, the calibration of models is done by the methods presented in the Nlopt library with considering the same objective functions and the same initial guess for each parameter. Then, a home-made finite element simulator is used to evaluate the correctness of the proposed material parameters identification framework. The results show the capability of the proposed optimization framework to determine properly the material parameters of multivariable plasticity models.

show abstract

Section: Introductionmentioning

confidence: 99%

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Sanei,

Devloo,

Forti

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The finite element method usually gives the final results anyway regardless of the mesh quality in the preprocessing steps. Therefore, users of the finite element analysis must be a domain expert to justify the correctness and accuracy of the results [14].…”

Section: Introductionmentioning

confidence: 99%

Time-Continuous Energy-Conservation Neural Network for Structural Dynamics Analysis

Yuan

Wang

Yang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Fast and accurate structural dynamics analysis is important for structural design and damage assessment. Structural dynamics analysis leveraging machine learning techniques has become a popular research focus in recent years. Although the basic neural network provides an alternative approach for structural dynamics analysis, the lack of physics law inside the neural network limits the model accuracy and fidelity. In this paper, a new family of the energy-conservation neural network is introduced, which respects the physical laws. The neural network is explored from a fundamental single-degree-of-freedom system to a complicated multiple-degrees-of-freedom system. The damping force and external forces are also considered step by step. To improve the parallelization of the algorithm, the derivatives of the structural states are parameterized with the novel energy-conservation neural network instead of specifying the discrete sequence of structural states. The proposed model uses the system energy as the last layer of the neural network and leverages the underlying automatic differentiation graph to incorporate the system energy naturally, which ultimately improves the accuracy and long-term stability of structures dynamics response calculation under an earthquake impact. The trade-off between computation accuracy and speed is discussed. As a case study, a 3-story building earthquake simulation is conducted with realistic earthquake records.

show abstract

An Innovative Scheme to Make an Initial Guess for Iterative Optimization Methods to Calibrate Material Parameters of Strain-Hardening Elastoplastic Models

et al. 2021

View full text Add to dashboard Cite

Procedures to build trust in nonlinear elastoplastic integration algorithm: solution and code verification

Cited by 6 publications

References 31 publications

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Calibration of multivariable plasticity parameters using classical optimization methods for porous rocks

Time-Continuous Energy-Conservation Neural Network for Structural Dynamics Analysis

An Innovative Scheme to Make an Initial Guess for Iterative Optimization Methods to Calibrate Material Parameters of Strain-Hardening Elastoplastic Models

Contact Info

Product

Resources

About