Novel Evolutionary-Optimized Neural Network for Predicting Fresh Concrete Slump

Hamed Safayenikoo, Mohammad Khajehzadeh and Moncef L. Nehdi
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Hamed Safayenikoo: Department of Civil Engineering, Chabahar Maritime University, Chabahar 99717-78631, Iran
Mohammad Khajehzadeh: Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar 77419-43615, Iran
Moncef L. Nehdi: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, Canada

Sustainability, 2022, vol. 14, issue 9, 1-15

Abstract: Accurate prediction of fresh concrete slumps is a complex non-linear problem that depends on several parameters including time, temperature, and shear history. It is also affected by the mixture design and various concrete ingredients. This study investigates the efficiency of three novel integrative approaches for predicting this parameter. To this end, the vortex search algorithm (VSA), multi-verse optimizer (MVO), and shuffled complex evolution (SCE) are used to optimize the configuration of multi-layer perceptron (MLP) neural network. The optimal complexity of each model was appraised via sensitivity analysis. Various statistical metrics revealed that the accuracy of the MLP was increased after coupling it with the above metaheuristic algorithms. Based on the obtained results, the prediction error of the MLP was decreased by up to 17%, 10%, and 33% after applying the VSA, MVO, and SCE, respectively. Moreover, the SCE emerged as the fastest optimizer. Accordingly, the novel explicit formulation of the SCE-MLP was introduced as a capable model for the practical estimation of fresh concrete slump, which can assist in project planning and management.

Keywords: neural network; metaheuristic optimization; shuffled complex evolution; concrete; slump; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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