Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence

This paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sour...

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Bibliographic Details
Published in:Advances in civil engineering 2020, Vol.2020 (2020), p.1-12
Main Authors: Vu, Hoang Hiep, Pham Thanh, Tung, Pham Duy, Hoa, Nguyen, Tu T.
Format: Article
Language:English
Subjects:
Adaptive systems
Aggregates
Algorithms
Artificial intelligence
Artificial neural networks
Civil engineering
Compressive strength
Concrete
Fiber reinforced concretes
Fuzzy sets
High strength
Mathematical models
Neural networks
Neurons
Outdoor air quality
Parameter sensitivity
Self-compacting concrete
Sensitivity analysis
Shear strength
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Summary:This paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R2) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC.
ISSN:1687-8086
1687-8094
DOI:10.1155/2020/3012139