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Structural Engineering and Mechanics Volume 60, Number 4, November25 2016 , pages 655-665 DOI: https://doi.org/10.12989/sem.2016.60.4.655 |
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Prediction of behavior of fresh concrete exposed to vibration using artificial neural networks and regression model |
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Gultekin Aktas and Mehmet Sirac Ozerdem
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| Abstract | ||
| 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. | ||
| Key Words | ||
| precast concrete mold; compaction of fresh concrete; vibration; modeling; artificial neural networks (ANNs); regression model | ||
| Address | ||
| Gultekin Aktas: Department of Civil Engineering, Dicle University, 21280 Diyarbakir, Turkey Mehmet Sirac Ozerdem: Department of Electrical and Electronics Engineering, Dicle University, 21280 Diyarbakir, Turkey | ||