- Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms. Dans: Case Studies in Construction Materials, v. 21 (décembre 2024). (2024):
- Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models. Dans: Case Studies in Construction Materials, v. 20 (juillet 2024). (2024):
- Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms. Dans: Case Studies in Construction Materials, v. 20 (juillet 2024). (2024):
- Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms. Dans: Case Studies in Construction Materials, v. 20 (juillet 2024). (2024):
- Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete. Dans: Developments in the Built Environment, v. 17 (mars 2024). (2024):
- Forecasting the Strength Characteristics of Concrete incorporating Waste Foundry Sand using advance machine algorithms including deep learning. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023). (2023):
- Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023). (2023):
- Predicting ultra-high-performance concrete compressive strength using gene expression programming method. Dans: Case Studies in Construction Materials, v. 18 (juillet 2023). (2023):
- Space cooling achievement by using lower electricity in hot months through introducing PCM-enhanced buildings. Dans: Journal of Building Engineering, v. 53 (août 2022). (2022):
- Per- and Polyfluoroalkyl Substances Presence, Pathways, and Cycling through Drinking Water and Wastewater Treatment. Dans: Journal of Environmental Engineering (ASCE), v. 148, n. 1 (janvier 2022). (2022):