Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression
| Autor: | Shreenivas Londhe, Sayed Mohammadmehdi Jamal, Faezehossadat Khademi, Neela Deshpande |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Environmental Engineering 0211 other engineering and technologies 02 engineering and technology Mix design MLR 021105 building & construction Linear regression ANFIS Adaptive neuro fuzzy inference system Aggregate (composite) Artificial neural network Renewable Energy Sustainability and the Environment business.industry Ecological Modeling Structural engineering 021001 nanoscience & nanotechnology Urban Studies Compressive strength Properties of concrete Data-driven models ANN Recycled aggregate concrete 0210 nano-technology business Quality assurance |
| Zdroj: | International Journal of Sustainable Built Environment. 5:355-369 |
| ISSN: | 2212-6090 |
| DOI: | 10.1016/j.ijsbe.2016.09.003 |
| Popis: | Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined. |
| Databáze: | OpenAIRE |
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