Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00799-8.pdf
Reference50 articles.
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2. Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P., & Pilakoutas, K. (2021). Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research, 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449
3. Bellavista, P., Corradi, A., Fanelli, M., & Foschini, L. (2012). A survey of context data distribution for mobile ubiquitous systems. ACM Computing Surveys, 44, 1–45. https://doi.org/10.1145/2333112.2333119
4. Brooks, S. P., & Morgan, B. J. T. (1995). Optimization using simulated annealing. Journal of the Royal Statistical Society: Series D (The Statistician), 44, 241–257. https://doi.org/10.2307/2348448
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