Evaluating the Compressive Strength of Recycled Aggregate Concrete Using Novel Artificial Neural Network

Kennedy C. Onyelowe, Tammineni Gnananandarao, Ahmed M. Ebid, Hisham A. Mahdi, M. Razzaghian Ghadikolaee, Mohammed Al-Ajamee


In this work, the compressive strength of concrete made from recycled aggregate is studied and an intelligent prediction is proposed by using a novel artificial neural network (ANN), which utilizes a sigmoid function and enables the proposal of closed-form equations. An extensive literature search was conducted, which gave rise to 476 data points containing cement, sand, aggregates, recycled aggregates of fine to coarse texture, water, and plasticizer as the constituents of the concrete and the input variables of the intelligent model. The compressive strength (fc) of the recycled aggregate concrete (RAC), which was studied through multiple experiments, was the output variable of the model. The data points of concrete strength collected through literature show a consistent and sustained strength improvement with the increase in the recycled aggregate proportions. However, the outcome of the concrete compressive strength predictive model shows remarkable performance indices as follows; r is 0.99 and 0.99, R2is 0.98 and 0.97, MSE is 28.67% and 44.64%, RMSE is 5.35% and 6.68%, MAE is 4.12% and 5.01%, and MAPE is 12.73% and 13.83% for the model training and testing respectively. These results compared well with previous studies conducted on RAC with less data, different activation functions, and different techniques. Generally, the closed-form equation, which performed at an average accuracy of 97.5% with an internal consistency of 99%, has shown its potential to be applied in RAC design and construction activities for a sustainable performance evaluation of recycled aggregate concrete.


Doi: 10.28991/CEJ-2022-08-08-011

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Compressive Strength; Recycled Aggregate Concrete; Sustainable Construction; Eco-friendly Concrete.


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DOI: 10.28991/CEJ-2022-08-08-011


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Copyright (c) 2022 Kennedy C. ONYELOWE, Tammineni GNANANANDARAO, Ahmed M. Ebid, Hisham A. MAHDI, Mehrdad RAZZAGHIAN GHADIKOLAEE, Mohammed AL-AJAMEE

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