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- Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete. Dans: Case Studies in Construction Materials, v. 21 (décembre 2024). (2024):
- Enhancing Najran’s sustainable smart city development in the face of urbanization challenges in Saudi- Arabia. Dans: Journal of Asian Architecture and Building Engineering. :
- 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):
- Influence of wastepaper sludge ash as partial cement replacement on the properties of lightweight foamed concrete. Dans: Journal of Building Engineering, v. 79 (novembre 2023). (2023):
- 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):
- 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):
- Building construction supply chain resilience under supply and demand uncertainties. Dans: Automation in Construction, v. 158 (février 2024). (2024):
- Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023). (2023):
- Development of rubberised cementitious material incorporating graphene nanoplatelets and silica fume. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023). (2023):
- Effect of agricultural olive, rice husk and sugarcane leaf waste ashes on sustainable ultra-high-performance concrete. Dans: Journal of Building Engineering, v. 72 (août 2023). (2023):