BAT Algorithm-Based ANN to Predict the Compressive Strength of Concrete—A Comparative Study
| Autor: | Chiara Bedon, Amir Hasanzade-Inallu, Mehdi Nikoo, Nasrin Aalimahmoody, Nasim Hasanzadeh-Inanlou |
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| Přispěvatelé: | Aalimahmoody, Nasrin, Bedon, Chiara, Hasanzadeh-Inanlou, Nasim, Hasanzade-Inallu, Amir, Nikoo, Mehdi |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer science Computer Science::Neural and Evolutionary Computation 0211 other engineering and technologies 02 engineering and technology genetic algorithm (GA) Teaching-Learning-Based-Optimization (TLBO) compressive strength of concrete 021105 building & construction Genetic algorithm 0202 electrical engineering electronic engineering information engineering General Materials Science BAT algorithm (BAT) multi linear regression (MLR) model Bat algorithm Civil and Structural Engineering Artificial neural network business.industry artificial neural network (ANN) Regression analysis Building and Construction Structural engineering Geotechnical Engineering and Engineering Geology Computer Science Applications Nonlinear system Compressive strength 020201 artificial intelligence & image processing business |
| Zdroj: | Infrastructures Volume 6 Issue 6 Infrastructures, Vol 6, Iss 80, p 80 (2021) |
| ISSN: | 2412-3811 |
| DOI: | 10.3390/infrastructures6060080 |
| Popis: | The number of effective factors and their nonlinear behaviour—mainly the nonlinear effect of the factors on concrete properties—has led researchers to employ complex models such as artificial neural networks (ANNs). The compressive strength is certainly a prominent characteristic for design and analysis of concrete structures. In this paper, 1030 concrete samples from literature are considered to model accurately and efficiently the compressive strength. To this aim, a Feed-Forward (FF) neural network is employed to model the compressive strength based on eight different factors. More in detail, the parameters of the ANN are learned using the bat algorithm (BAT). The resulting optimized model is thus validated by comparative analyses towards ANNs optimized with a genetic algorithm (GA) and Teaching-Learning-Based-Optimization (TLBO), as well as a multi-linear regression model, and four compressive strength models proposed in literature. The results indicate that the BAT-optimized ANN is more accurate in estimating the compressive strength of concrete. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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