Study on predicting compressive strength of concrete using supervised machine learning techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00662-w.pdf
Reference54 articles.
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2. Azimi-Pour, M., Eskandari-Naddaf, H., & Pakzad, A. (2020). Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2019.117021
3. Biswas, R., Samui, P., & Rai, B. (2019). Determination of compressive strength using relevance vector machine and emotional neural network. Asian Journal of Civil Engineering, 20, 1109–1118. https://doi.org/10.1007/s42107-019-00171-9
4. BKA, M. A. R., Ngamkhanong, C., Wu, Y., & Kaewunruen, S. (2021). Recycled aggregates concrete compressive strength prediction using artificial neural networks (ANNs). Infrastructures 6 (2) 17. https://doi.org/10.3390/infrastructures6020017
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