Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

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ID: 111448
2019
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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.
Reference Key
moayedi2019appliedapplication Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hossein Moayedi;Bahareh Kalantar;Loke Kok Foong;Dieu Tien Bui;Alireza Motevalli;Moayedi, Hossein;Kalantar, Bahareh;Foong, Loke Kok;Tien Bui, Dieu;Motevalli, Alireza;
Journal applied sciences
Year 2019
DOI
10.3390/app9204340
URL
https://doi.org/10.3390/app9204340
Keywords
concrete neural computing slump ant lion optimizer

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