We present a new method to model fracture of concrete based on energy minimisation.The concrete is considered on the mesoscale as composite consisting of cement paste,aggregates and micro pores.In this first step,the ...We present a new method to model fracture of concrete based on energy minimisation.The concrete is considered on the mesoscale as composite consisting of cement paste,aggregates and micro pores.In this first step,the alkali-silica reaction is taken into account through damage mechanics though the process is more complex involving thermo-hygro-chemo-mechanical reaction.We use a non-local damage model that ensures the wellposedness of the boundary value problem(BVP).In contrast to existing methods,the interactions between degrees of freedom evolve with the damage evolutions.Numerical results are compared to analytical and experimental results and show good agreement.展开更多
Materials’microstructures are signatures of their alloying composition and processing history.Automated,quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches....Materials’microstructures are signatures of their alloying composition and processing history.Automated,quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches.However,their shortcomings are poor data efficiency and domain generalizability across data sets,inherently conflicting the expenses associated with annotating data through experts,and extensive materials diversity.To tackle both,we propose to apply a sub-class of transfer learning methods called unsupervised domain adaptation(UDA).UDA addresses the task of finding domain-invariant features when supplied with annotated source data and unannotated target data,such that performance on the latter is optimized.Exemplarily,this study is conducted on a lath-shaped bainite segmentation task in complex phase steel micrographs.Domains to bridge are selected to be different metallographic specimen preparations and distinct imaging modalities.We show that a state-of-the-art UDA approach substantially fosters the transfer between the investigated domains,underlining this technique’s potential to cope with materials variance.展开更多
文摘We present a new method to model fracture of concrete based on energy minimisation.The concrete is considered on the mesoscale as composite consisting of cement paste,aggregates and micro pores.In this first step,the alkali-silica reaction is taken into account through damage mechanics though the process is more complex involving thermo-hygro-chemo-mechanical reaction.We use a non-local damage model that ensures the wellposedness of the boundary value problem(BVP).In contrast to existing methods,the interactions between degrees of freedom evolve with the damage evolutions.Numerical results are compared to analytical and experimental results and show good agreement.
文摘Materials’microstructures are signatures of their alloying composition and processing history.Automated,quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches.However,their shortcomings are poor data efficiency and domain generalizability across data sets,inherently conflicting the expenses associated with annotating data through experts,and extensive materials diversity.To tackle both,we propose to apply a sub-class of transfer learning methods called unsupervised domain adaptation(UDA).UDA addresses the task of finding domain-invariant features when supplied with annotated source data and unannotated target data,such that performance on the latter is optimized.Exemplarily,this study is conducted on a lath-shaped bainite segmentation task in complex phase steel micrographs.Domains to bridge are selected to be different metallographic specimen preparations and distinct imaging modalities.We show that a state-of-the-art UDA approach substantially fosters the transfer between the investigated domains,underlining this technique’s potential to cope with materials variance.
