Ehsan Adeli

Dr.-Ing. Ehsan Adeli

Bayesian Parameter Identification of a Viscoplastic Model

Being a major matter of dispute, on one hand, steel structures have been attracting more and more attention from scientific societies especially during the recent decades. On the other hand, development of computers and various numerical methods has brought up the important issue of trustability and capabilities of different modeling techniques and analysis methods.

The evaluation of the performance of engineering structures includes models of behavior of materials, structural elements, loadings, external excitations etc. In assessment studies, there are several classes of uncertainty related to the lack of information on loading conditions/excitations, behavior of material properties over time, geometry and boundary conditions which may be identified and reduced by the means of quality control or system monitoring and identification.

Considering the mentioned facts, my research interests are mainly oriented towards identification of material properties of steel structure at high temperature described by a hysteretic model under loadings/excitations. In other words, the goal of my research is to apply some approaches in order to solve the inverse problem, bearing in mind that the recent numerical techniques for inversion provide a realistic possibility for complex mathematical models compared to traditional methods with too computationally demanding. Moreover, the optimization of experimental evaluations will be studied due to obtain much information from the measurement of structure with investigation of optimal sensor placements.

Having given a brief general summary of my research field, I would end up this introduction with a number of most important problems with which I am dealing and everyone is warmly welcomed to contact me for any further inquiries:

  • Constitutive models of Viscoplasticity for metals with hysteresis behavior
  • Bayes' rule, inverse problem and identification
  • Stochastic Finite Element Method (SFEM)
  • Uncertainty Quantification (UQ)
  • Markov Chain Monte Carlo (MCMC) method
  • Polynomial Chaos Expansion (PCE)
  • Design of experiment and optimal placement of sensors

Publications within the framework of the RTG:

Doctoral thesis:

E. Adeli. Viscoplastic-Damage Model Parameter Identification via Bayesian Methods.

Publications in peer-reviewed scientific journals:

E. Adeli, B. Rosić, H.G. Matthies and S. Reinstädler. Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods. QUIET, 2018.

E. Adeli and H.G. Matthies. Parameter Identification in Viscoplasticity using Transitional Markov Chain Monte Carlo Method. Probabilistic Engineering Mechanics, 2019.

E. Adeli, H.G. Matthies, S. Reinstädler and D. Dinkler. Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage. DOI: 10.13140/RG.2.2.30280.26889, 2019.

E. Adeli, H.G. Matthies, S. Reinstädler and D. Dinkler. Bayesian Parameter Determination of a CT-Test described by a Viscoplastic-Damage Model considering the Model Error. DOI: 10.13140/RG.2.2.26924.82562, 2019.

Conference contribution with publication in conference proceedings:

E. Adeli, B. Rosić, H.G. Matthies and S. Reinstädler. Bayesian Parameter Identification in Plasticity. XIV International Conference on Computational Plasticity, COMPLAS 2017.

Conference contribution without publication in conference proceedings:

Bayesian Estimation of Steel Material Properties under Cyclic Loading Conditions. WCSMO12 Congress, Braunschweig, Germany, 2017.

Bayesian Estimation of Steel Material Properties under Cyclic Loading Conditions. PARAMetric UNCertainty Summer School and Workshop, Budapest, Hungary, 2017.

Bayesian Parameter Identification in Plasticity (Poster). QUIET 2017 Workshop, Trieste, Italy, 2017.

Identification of a Visco-plastic Model with Uncertain Parameters using Bayesian Methods. UNCECOMP 2017 Conference, Rhodes Island, Greece, 2017.