Determination of material parameters of alkali-activated concrete using specimens with different types of notches

Determination of material parameters of alkali-activated concrete using specimens with different types of notches

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The paper is focused on the determination of mechanical fracture parameters of alkali-activated concrete (AAC) specimens of nominal dimensions 80 × 80 × 240 mm with central notches of the following types: straight-through notch, chevron notch with 90° notch angle and chevron notch with 120° notch angle. In accordance with the results of the experimental research, a numerical simulation of the fracture test of the specimens tested in three-point bending was carried out. The 3D FEM computational model is based on the configuration of the laboratory test and was developed in ATENA-GiD software. Artificial neural network (ANN)-based inverse analysis was used to identify the mechanical fracture parameters of studied composite. A neural network ensemble was created, with one robust network for each type of notch, covering the entire domain of identified parameters. The main goal of this paper is to present the results of the identification of the mechanical fracture parameters of the AAC and their comparison with the values obtained by direct evaluation of the experiments. Another objective is to investigate the influence of the notch type on the mechanical fracture parameters of the AAC.

The paper is focused on the determination of mechanical fracture parameters of alkali-activated concrete (AAC) specimens of nominal dimensions 80 × 80 × 240 mm with central notches of the following types: straight-through notch, chevron notch with 90° notch angle and chevron notch with 120° notch angle. In accordance with the results of the experimental research, a numerical simulation of the fracture test of the specimens tested in three-point bending was carried out. The 3D FEM computational model is based on the configuration of the laboratory test and was developed in ATENA-GiD software. Artificial neural network (ANN)-based inverse analysis was used to identify the mechanical fracture parameters of studied composite. A neural network ensemble was created, with one robust network for each type of notch, covering the entire domain of identified parameters. The main goal of this paper is to present the results of the identification of the mechanical fracture parameters of the AAC and their comparison with the values obtained by direct evaluation of the experiments. Another objective is to investigate the influence of the notch type on the mechanical fracture parameters of the AAC.

Inverse analysis, alkali-activated concrete, numerical simulation, straight-through notch, chevron notch

Inverse analysis, alkali-activated concrete, numerical simulation, straight-through notch, chevron notch

LIPOWCZAN, M.; MIARKA, P.; LEHKÝ, D.

2025

07.06.2024

American Institute of Physics

Melville NY, USA

978-0-7354-4954-1

AIP Conference Proceedings

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