ArticleMaterials (Basel, Switzerland)2021
Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Strength and Ductility Enhancement in Coarse-Aggregate UHPC via Fiber Hybridization: Micro-Mechanistic Insights and Artificial Neural Network Prediction.Materials (Basel, Switzerland) · 2026Article
- Optimizing compressive strength prediction using adversarial learning and hybrid regularization.Scientific reports · 2024Article
- Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC's Compressive Strength.Materials (Basel, Switzerland) · 2023Article
- Article
- Feasibility Study of Pervious Concrete with Ceramsite as Aggregate Considering Mechanical Properties, Permeability, and Durability.Materials (Basel, Switzerland) · 2023Article
- Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
Recycled aggregate concrete (RAC), due to its high porosity and the residual cement and mortar on its surface, exhibits weaker strength than common concrete. To guarantee the safe use of RAC, a compressive strength prediction model based on artificial neural network (ANN) was built in this paper, which can be applied to predict the RAC compressive strength for 28 days. A data set containing 88 data points was obtained by relative tests with different mix proportion designs. The data set was used to develop an ANN, whose optimal structure was determined using the trial-and-error method by taking cement content (C), sand content (S), natural coarse aggregate content (NCA), recycled coarse aggregate content (RCA), water content (W), water-colloid ratio (WCR), sand content rate (SR), and replacement rate of recycled aggregate (RRCA) as input parameters. On the basis of different numbers of hidden layers, numbers of hidden layer neurons, and transfer functions, a total of 840 different back propagation neural network (BPNN) models were developed using MATLAB software, which were then sorted according to the correlation coefficient R
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