Bayesian calibration of lattice discrete particle model for concrete

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F16%3A00243298" target="_blank" >RIV/68407700:21110/16:00243298 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian calibration of lattice discrete particle model for concrete

Popis výsledku v původním jazyce

In lattice or particle formulations of models for quasi-brittle materials, a medium is discretized ``a priori'' according to an idealization of its internal structure. Geometrical parameters of particles or lattice equip these type of formulations with inherent characteristic lengths and they have the intrinsic ability of simulating the geometrical features of material internal structure. This allows the accurate simulation of damage initiation and crack propagation at various length scales, however, at increased computational costs. Here we employ the so-called Lattice Discrete Particle Model (LDPM). LDPM was calibrated, and validated against a large variety of loading conditions in both quasi-static and dynamic loading conditions and it was demonstrated to possess superior predictive capability. Nevertheless, the utilized calibration procedure was based on a hand-fitting, which complicates further practical applications of the model. Here we present a Bayesian inference of model parameters from experimental data obtained from notched three-point-bending tests and cube compression tests. The Bayesian inference allows to solve the inverse problem as well-possed and to quantify posterior uncertainty in parameters by combining a prior knowledge about the realistic parameter values and uncertainty contained in measurement errors. In particular, we obtain the posterior distributions by robust the Markov chain Monte Carlo sampling, where the computational burden, arising from repeated model simulations, is overcome by using a polynomial chaos-based surrogate of the LDPM.

Název v anglickém jazyce

Bayesian calibration of lattice discrete particle model for concrete

Popis výsledku anglicky

In lattice or particle formulations of models for quasi-brittle materials, a medium is discretized ``a priori'' according to an idealization of its internal structure. Geometrical parameters of particles or lattice equip these type of formulations with inherent characteristic lengths and they have the intrinsic ability of simulating the geometrical features of material internal structure. This allows the accurate simulation of damage initiation and crack propagation at various length scales, however, at increased computational costs. Here we employ the so-called Lattice Discrete Particle Model (LDPM). LDPM was calibrated, and validated against a large variety of loading conditions in both quasi-static and dynamic loading conditions and it was demonstrated to possess superior predictive capability. Nevertheless, the utilized calibration procedure was based on a hand-fitting, which complicates further practical applications of the model. Here we present a Bayesian inference of model parameters from experimental data obtained from notched three-point-bending tests and cube compression tests. The Bayesian inference allows to solve the inverse problem as well-possed and to quantify posterior uncertainty in parameters by combining a prior knowledge about the realistic parameter values and uncertainty contained in measurement errors. In particular, we obtain the posterior distributions by robust the Markov chain Monte Carlo sampling, where the computational burden, arising from repeated model simulations, is overcome by using a polynomial chaos-based surrogate of the LDPM.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GJ16-11473Y" target="_blank" >GJ16-11473Y: Identifikace aleatorické nejistoty v parametrech heterogenních materiálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů