- An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete. Dans: Construction and Building Materials, v. 449 (octobre 2024). (2024):
- Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes. Dans: Developments in the Built Environment, v. 19 (octobre 2024). (2024):
- Use of explainable machine learning models in blast load prediction. Dans: Engineering Structures, v. 312 (août 2024). (2024):
- Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning. Dans: Journal of Building Engineering, v. 85 (mai 2024). (2024):
- Exploring the applicability of expanded polystyrene (EPS) based concrete panels as roof slab insulation in the tropics. Dans: Case Studies in Construction Materials, v. 17 (décembre 2022). (2022):
- (2022): Influence of Crumb Rubber and Coconut Coir on Strength and Durability Characteristics of Interlocking Paving Blocks. Dans: Buildings, v. 12, n. 7 (5 juillet 2022).
- Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings. Dans: Journal of Wind Engineering and Industrial Aerodynamics, v. 226 (juillet 2022). (2022):
- Investigating applicability of sawdust and retro-reflective materials as external wall insulation under tropical climatic conditions. Dans: Asian Journal of Civil Engineering, v. 23, n. 4 (avril 2022). (2022):
- A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Dans: Case Studies in Construction Materials, v. 16 (juin 2022). (2022):
- On the deviation of mean pressure coefficients in wind loading standards for a low-rise, gable-roofed building with boundary walls. Dans: Structures, v. 36 (février 2022). (2022):