Compressive strength prediction of recycled concrete based on deep learning
By Fangming Deng, Yigang He, S. Zhou, Y. Yu, Haigen Cheng, Xiang Wu. Construction and Building Materials, 2018
366 citations recorded by the source corpus.
Abstract Considering on the current difficulties of predicting the compressive strength of recycled aggregate concrete, this paper proposes a prediction model based on deep learning theory. First, the deep features of water-cement ratio, recycled coarse aggregate replacement ratio, recycled fine aggregate replacement ratio, fly ash replacement ratio as well as their combinations are learned through a convolutional neural networks. Then, the prediction model is developed using the softmax regression. 74 sets of concrete block masonry with different mix ratios are used in the experiments and the results show that the prediction model based on deep learning exhibits the advantages including higher precision, higher efficiency and higher generalization ability compared with the traditional neural network model, and could be considered as a new method for calculating the strength of recycled concrete.
Subfields: Building and Construction, Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/1ce44e3af8e4fb9962f5e4bcd30f4301fb17555e
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Citations in the corpus
- A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, methodology): “In the present study, WA, WCR, FA, RCA, NCA, and WTMR were considered as the input variables, whereas the CS of RAC at 28 days was selected as the output variable.”