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Strength prediction of paste filling material based on convolutional neural network
Author(s) -
Cheng Haigen,
Hu Junjian,
Hu Chen,
Deng Fangming
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12373
Subject(s) - aggregate (composite) , convolutional neural network , computer science , environmentally friendly , measure (data warehouse) , artificial neural network , raw material , environmental science , process engineering , data mining , artificial intelligence , engineering , materials science , ecology , chemistry , organic chemistry , composite material , biology
The common backfill mining technology in the green mining industry can be used for the secondary utilization of construction waste in smart cities. This measure has the advantages of low cost, fast results, and less environmental pollution. Over the past few decades, with the continuous advancement of global urbanization, the effective and environmentally friendly construction waste disposal and emission are very important for the development of urban green construction. Construction waste can be prepared as paste filling material, as one of the raw materials for backfill mining. This paper proposes a new method that can quickly and accurately predict the strength of paste filling materials with different compositions. A deep connected convolutional neural network (CNN) that can extract input parameters is used to build a prediction model. The coarse aggregate, fine aggregate, and cementing material are employed as the input variables of the CNN model, and five indicators which are generally used to evaluate the strength of filling material are selected as the output results. The experimental results show that the proposed prediction approach can obtain robust prediction results and high prediction accuracy and speed.