Predicting and optimizing the concrete compressive strength using an explainable boosting machine learning model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00848-2.pdf
Reference36 articles.
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3. Cheng, M. Y., Firdausi, P. M., & Prayogo, D. (2014). High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT). Engineering Applications of Artificial Intelligence, 29, 104–113. https://doi.org/10.1016/j.engappai.2013.11.014
4. Chou, J.-S., Chiu, C.-K., Farfoura, M., & Al-Taharwa, I. (2011). Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. Journal of Computing in Civil Engineering, 25(3), 242–253. https://doi.org/10.1061/(asce)cp.1943-5487.0000088
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