Evaluation of artificial intelligence methods based on metaheuristic algorithms for estimating the compressive strength of lightweight structural concrete containing leca
Compressive strength (CS) is an important mechanical characteristic of concrete, which is highly perceptible from concrete mix design. The main objective of this study is investigate metaheuristic algorithms, combined with artificial intelligence methods (hybrid models), so as to provide relationships to estimate compressive strength of lightweight expanded clay aggregate (LECA) concrete, by comparing other Artificial inteligence models (AI models), assessing effective parameters and model accuracy and finally i validatt the result of the developed models. In this study, 405 laboratory samples in their 7, 28 and 90 days old age were used. Presented models in this study were expanded using artificial neural network (ANN), multivariate adaptive regression splines (MARS) and improved MARS with crow search algorithm (MARS-CSA). Statistical indicator named R, RMSE and MAE investigated results of the developed models. In testing performance, predicted compressive strength values of MARS-CSA (R= 0.913, RMSE= 39.002) indicated significant accuracy in comparison with MARS and ANN models.
Ashrafian, A., Taheri Amiri, M. J., Maleki, N., & Jamali, M. (2020). Evaluation of artificial intelligence methods based on metaheuristic algorithms for estimating the compressive strength of lightweight structural concrete containing leca. Asas Journal, 22(58), 19-34.
MLA
Ali Ashrafian; Mohammad Javad Taheri Amiri; Niloofar Maleki; Milad Jamali. "Evaluation of artificial intelligence methods based on metaheuristic algorithms for estimating the compressive strength of lightweight structural concrete containing leca". Asas Journal, 22, 58, 2020, 19-34.
HARVARD
Ashrafian, A., Taheri Amiri, M. J., Maleki, N., Jamali, M. (2020). 'Evaluation of artificial intelligence methods based on metaheuristic algorithms for estimating the compressive strength of lightweight structural concrete containing leca', Asas Journal, 22(58), pp. 19-34.
VANCOUVER
Ashrafian, A., Taheri Amiri, M. J., Maleki, N., Jamali, M. Evaluation of artificial intelligence methods based on metaheuristic algorithms for estimating the compressive strength of lightweight structural concrete containing leca. Asas Journal, 2020; 22(58): 19-34.
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