- Assessing the compressive strength of eco-friendly concrete made with rice husk ash: A hybrid artificial intelligence-aided technique. Dans: Structures, v. 68 (octobre 2024). (2024):
- Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework. Dans: Construction and Building Materials, v. 409 (décembre 2023). (2023):
- Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network. Dans: Structural Concrete, v. 25, n. 2 (janvier 2024). (2024):
- Towards sustainable use of foundry by-products: Evaluating the compressive strength of green concrete containing waste foundry sand using hybrid biogeography-based optimization with artificial neural networks. Dans: Journal of Building Engineering, v. 76 (octobre 2023). (2023):
- New insight into the prediction of strength properties of cementitious mortar containing nano‐ and micro‐silica based on porosity using hybrid artificial intelligence techniques. Dans: Structural Concrete, v. 24, n. 4 (5 juillet 2023). (2023):
- Hybrid artificial neural network with biogeography-based optimization to assess the role of cement fineness on ecological footprint and mechanical properties of cement mortar expose to freezing/thawing. Dans: Construction and Building Materials, v. 304 (octobre 2021). (2021):
- Effect of cement strength class on the generalization of Abrams' law. Dans: Structural Concrete, v. 20, n. 1 (février 2019). (2019):
- ANN prediction of cement mortar compressive strength, influence of cement strength class. Dans: Construction and Building Materials, v. 138 (mai 2017). (2017):