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Application of Three Metaheuristic Techniques in Simulation of Concrete Slump.

Authors :
Moayedi, Hossein
Kalantar, Bahareh
Foong, Loke Kok
Tien Bui, Dieu
Motevalli, Alireza
Source :
Applied Sciences (2076-3417); Oct2019, Vol. 9 Issue 20, p4340, 15p
Publication Year :
2019

Abstract

Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
9
Issue :
20
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
139415170
Full Text :
https://doi.org/10.3390/app9204340