• Structural Engineering and Mechanics
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• 제57권3호
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• pp.425-439
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• 2016

This paper deals with the behavior of fresh concrete that is under vibration using mass-spring model (MSM). To this end, behaviors of two different full scale precast concrete molds were investigated experimentally and theoretically. Experiments were performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while mold is empty and full of fresh concrete. Analytical modeling of molds used in experiments were prepared by three dimensional finite element method (3D FEM) using software. Modeling of full mold, using MSM, was made to solve the problem of dynamic interaction between fresh concrete and mold. Numerical displacement histories obtained from time history analysis were compared with experimental results. The comparisons show that the measured and computed results are compatible.

• Structural Engineering and Mechanics
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• 제74권4호
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• pp.559-565
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• 2020

This paper intends to progress models to accurately estimate the behavior of fresh concrete under vibration using artificial neural networks (ANNs). To this end, behavior of a full scale precast concrete mold was investigated numerically. Experimental study was carried out under vibration with the use of a computer-based data acquisition system. In this study measurements were taken at three points using two vibrators. Transducers were used to measure time-dependent lateral displacements at these points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using ANNs. Benefiting ANNs used in this study for modeling fresh concrete, mold design can be performed. For the modeling of ANNs: Experimental data were divided randomly into two parts such as training set and testing set. Training set was used for ANN's learning stage. And the remaining part was used for testing the ANNs. Finally, ANN modeling was compared with measured data. The comparisons show that the experimental data and ANN results are compatible.

• Structural Engineering and Mechanics
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• 제60권4호
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• pp.655-665
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• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.