Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste
| Autor: | Mahmood Karimaei, Haleh Rasekh, Hamzeh Sadeghi, Farshad Dabbaghi, Maria Rashidi, F. Qaderi, Moncef L. Nehdi |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Geography
Planning and Development 0211 other engineering and technologies TJ807-830 Computational intelligence 02 engineering and technology Management Monitoring Policy and Law TD194-195 coal waste Renewable energy sources response surface methodology Flexural strength 021105 building & construction Coal GE1-350 Response surface methodology mix design Process engineering Cement model Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry prediction 021001 nanoscience & nanotechnology Environmentally friendly Environmental sciences flexural strength Mixture designs Environmental science concrete Coal waste 0210 nano-technology business artificial neural network |
| Zdroj: | Sustainability, Vol 13, Iss 7506, p 7506 (2021) Sustainability Volume 13 Issue 13 |
| ISSN: | 2071-1050 |
| Popis: | Construction activities have been a primary cause for depleting natural resources and are associated with stern environmental impact. Developing concrete mixture designs that meet project specifications is time-consuming, costly, and requires many trial batches and destructive tests that lead to material wastage. Computational intelligence can offer an eco-friendly alternative with superior accuracy and performance. In this study, coal waste was used as a recycled additive in concrete. The flexural strength of a large number of mixture designs was evaluated to create an experimental database. A hybrid artificial neural network (ANN) coupled with response surface methodology (RSM) was trained and employed to predict the flexural strength of coal waste-treated concrete. In this process, four influential parameters including the cement content, water-to-cement ratio, volume of gravel, and coal waste replacement level were specified as independent input variables. The results show that concrete incorporating 3% recycled coal waste could be a competitive and eco-efficient alternative in construction activities while attaining a superior flexural strength of 6.7 MPa. The RSM-modified ANN achieved superior predictive accuracy with an RMSE of 0.875. Based on the experimental results and model predictions, estimating the flexural strength of concrete incorporating waste coal using the RSM-modified ANN model yielded superior accuracy and can be used in engineering practice to save the effort, cost, and material wastage associated with trial batches and destructive laboratory testing while producing mixtures with enhanced flexural strength. |
| Databáze: | OpenAIRE |
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