Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is l...

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Veröffentlicht in:Applied sciences Jg. 11; H. 2; S. 485
Hauptverfasser: Kandiri, Amirreza, Sartipi, Farid, Kioumarsi, Mahdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.01.2021
Schlagworte:
Aggregates
Artificial intelligence
artificial neural network
Cement
compressive strength
concrete
Datasets
Exploitation
genetic algorithm
Genetic algorithms
grasshopper optimization algorithm
Mechanical properties
Mutation
Optimization algorithms
salp swarm algorithm
ISSN:2076-3417, 2076-3417
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Zusammenfassung:Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.
Bibliographie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11020485