A Novel XGBoost and RF-Based Metaheuristic Models for Concrete Compression Strength
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6233-4_45
Reference18 articles.
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2. American Society for Testing and Materials, Standard Test Method for High-Strain Dynamic Testing of Deep Foundations, D 4945-08 (2010). www.astm.org. Accessed 11 Oct 2020.
3. Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125. https://doi.org/10.1016/J.ADVENGSOFT.2017.09.004
4. Biswas R, Rai B, Samui P, Roy SS (2020) Estimating concrete compressive strength using MARS, LSSVM and GP. Eng J 24:41–52. https://doi.org/10.4186/ej.2020.24.2.41
5. Biswas R, Rai B, Samui P (2021) Compressive strength prediction model of high-strength concrete with silica fume by destructive and non-destructive technique. Innov Infrastr Sol 6:1–14. https://doi.org/10.1007/S41062-020-00447-Z/METRICS
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