ANN-Based Fatigue Strength of Concrete under Compression

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we study the reduced capacity as a function of a given number of cycles...

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Bibliographic Details
Published in:Materials 2019-11, Vol.12 (22), p.3787
Main Authors: Abambres, Miguel, Lantsoght, Eva O L
Format: Article
Language:English
Subjects:
Artificial neural networks
Bridges
Coefficient of variation
Compressive strength
Concrete
Concrete structures
Crack propagation
Datasets
Design analysis
Experiments
Fatigue strength
Influence
Neural networks
Optimization
Predictions
Prestressed concrete
Reinforced concrete
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Summary:When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we study the reduced capacity as a function of a given number of cycles by means of artificial neural networks. We used an input database with 203 datapoints gathered from the literature. To find the optimal neural network, 14 features of neural networks were studied and varied, resulting in the optimal neural net. This proposed model resulted in a maximum relative error of 5.1% and a mean relative error of 1.2% for the 203 datapoints. The proposed model resulted in a better prediction (mean tested to predicted value = 1.00 with a coefficient of variation 1.7%) as compared to the existing code expressions. The model we developed can thus be used for the design and the assessment of concrete structures and provides a more accurate assessment and design than the existing methods.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma12223787