Using Noise to Generate the Material Structure of Concrete

Using Noise to Generate the Material Structure of Concrete

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The contribution deals with the description and demonstration of an algorithm for the generation of spatial geometry – the material structure of concrete. The term “material structure of concrete” is taken to mean a mix of randomly distributed aggregate and cement binder, the latter being used to bond the aggregate. In order for the generated material structure to be as realistic as possible, a photograph of the material which is to be created is used as the input (i.e. data source) for the algorithm. The generation process itself involves the use of spatial noise functions, which are able to generate simple patterns as well as very complex fractals. The algorithm is based on the principle that similarities can be found between a suitably placed section cut through a noise space and the abovementioned input for the algorithm – a real photograph of the material. The functionality and individual steps of the algorithm are presented via two examples of concrete generation with differently shaped aggregate grains.

The contribution deals with the description and demonstration of an algorithm for the generation of spatial geometry – the material structure of concrete. The term “material structure of concrete” is taken to mean a mix of randomly distributed aggregate and cement binder, the latter being used to bond the aggregate. In order for the generated material structure to be as realistic as possible, a photograph of the material which is to be created is used as the input (i.e. data source) for the algorithm. The generation process itself involves the use of spatial noise functions, which are able to generate simple patterns as well as very complex fractals. The algorithm is based on the principle that similarities can be found between a suitably placed section cut through a noise space and the abovementioned input for the algorithm – a real photograph of the material. The functionality and individual steps of the algorithm are presented via two examples of concrete generation with differently shaped aggregate grains.

Optimization algorithms, global optimization, objective function, concrete, material model, computational model, experimental data, four-point bend

Optimization algorithms, global optimization, objective function, concrete, material model, computational model, experimental data, four-point bend

HUŠEK, M.; KALA, J.; KRÁL, P.; HOKEŠ, F.

2019

30.06.2018

978-0-7354-1690-1

AIP Conference Proceedings

1551-7616

AIP conference proceedings

2018

1978

Spojené státy americké

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