Machine learning models to predict sewer concrete strength exposed to sulfide environments: unveiling the superiority of Bayesian-optimized prediction models
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41939-024-00561-w.pdf
Reference84 articles.
1. Ahmed A et al (2023) Hybrid BO-XGBoost and BO-RF models for the strength prediction of self-compacting mortars with parametric analysis. Materials 16(12):4366
2. Alaejos P, Bermudez MA (2011) Influence of seawater curing in standard and high-strength submerged concrete. J Mater Civ Eng 23(6):915–920
3. Alani AM, Faramarzi A (2014) An evolutionary approach to modelling concrete degradation due to sulphuric acid attack. Appl Soft Comput 24(1):985–993
4. Alexandridis A (2013) Evolving RBF neural networks for adaptive soft-sensor design. Int J Neural Syst 23:1350029
5. Amlashi AT et al (2022) Application of computational intelligence and statistical approaches for auto-estimating the compressive strength of plastic concrete. Eur J Environ Civ Eng 26(8):3459–3490
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