The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Compressive strength prediction of recycled concrete based on deep learning
Abstract
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.
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Cites
ImageNet classification with deep convolutional neural networks
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Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
Duan, Z. H., Kou, S. C., & Poon, C. S. (2013) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2012.04.063 · Cited by 509
Microwave-assisted beneficiation of recycled concrete aggregates
Akbarnezhad, A., Ong, K. C. G., Zhang, M. H., Tam, C. T., & Foo, T. W. J. (2011) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2011.03.038 · Cited by 476
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Cited by
Machine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
Deep learning in the construction industry: A review of present status and future innovations
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101827 · Cited by 537
Mohtasham Moein, M., Saradar, A., Rahmati, K., Ghasemzadeh Mousavinejad, S. H., Bristow, J., Aramali, V., & Karakouzian, M. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105444 · Cited by 391
Duan, J., Asteris, P. G., Nguyen, H., Bui, X.-N., & Moayedi, H. (2021) · Engineering with Computers · doi:10.1007/s00366-020-01003-0 · Cited by 357
Metadata
- Authors
- Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H., & Wu, X.
- Venue
- Construction and Building Materials
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 175
- Pages
- 562-569
- ISSN
- 0950-0618 (ISSN-L)
- OpenAlex
- W2801822775
- Cited by
- 391
- References
- 19
- FWCI
- 17.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H., & Wu, X. (2018). Compressive strength prediction of recycled concrete based on deep learning. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2018.04.169
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