Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste

被引:22
作者
Dabbaghi, Farshad [1 ]
Rashidi, Maria [2 ]
Nehdi, Moncef L. [3 ]
Sadeghi, Hamzeh [4 ]
Karimaei, Mahmood [1 ]
Rasekh, Haleh [5 ]
Qaderi, Farhad [1 ]
机构
[1] Babol Noshirvani Univ Technol, Fac Civil Engn, Babol 4714871167, Iran
[2] Western Sydney Univ, Ctr Infrastruct Engn, Penrith, NSW 2751, Australia
[3] Western Univ, Dept Civil & Environm Engn, London, ON N6G 5L1, Canada
[4] Amirkabir Univ Technol, Fac Civil Engn, Tehran 4714871167, Iran
[5] Univ Technol Sydney, Sch Civil & Environm Engn, Sydney, NSW 2007, Australia
关键词
concrete; coal waste; flexural strength; artificial neural network; response surface methodology; model; prediction; mix design; MECHANICAL-PROPERTIES; CEMENT; OPTIMIZATION; REPLACEMENT; PREDICTION; SLUDGE; RUBBER;
D O I
10.3390/su13137506
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:22
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