Sohaib Nazar 
- (2024): Explicable AI-based modeling for the compressive strength of metakaolin-derived geopolymers. Dans: Case Studies in Construction Materials, v. 21 (décembre 2024).
- (2024): Experimenting the effectiveness of waste materials in improving the compressive strength of plastic-based mortar. Dans: Case Studies in Construction Materials, v. 21 (décembre 2024).
- (2024): Predictive modelling of compressive strength of fly ash and ground granulated blast furnace slag based geopolymer concrete using machine learning techniques. Dans: Case Studies in Construction Materials, v. 20 (juillet 2024).
- (2024): Comparing the efficacy of GEP and MEP algorithms in predicting concrete strength incorporating waste eggshell and waste glass powder. Dans: Developments in the Built Environment, v. 17 (mars 2024).
- (2023): Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023).
- (2023): Evaluating the relevance of eggshell and glass powder for cement-based materials using machine learning and SHapley Additive exPlanations (SHAP) analysis. Dans: Case Studies in Construction Materials, v. 19 (décembre 2023).
- (2023): An integral approach for testing and computational analysis of glass powder in cementitious composites. Dans: Case Studies in Construction Materials, v. 18 (juillet 2023).
- (2023): Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations (SHAP). Dans: Construction and Building Materials, v. 377 (mai 2023).
- (2023): Formulation of estimation models for the compressive strength of concrete mixed with nanosilica and carbon nanotubes. Dans: Developments in the Built Environment, v. 13 (mars 2023).
- (2023): An evolutionary machine learning-based model to estimate the rheological parameters of fresh concrete. Dans: Structures, v. 48 (février 2023).
- (2022): Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques. Dans: Buildings, v. 12, n. 12 (1 décembre 2022).
- (2020): Semi-analytical model for compressive arch action capacity of RC frame structures. Dans: Structures, v. 27 (octobre 2020).
- (2020): Hybrid graphene oxide/carbon nanotubes reinforced cement paste: An investigation on hybrid ratio. Dans: Construction and Building Materials, v. 261 (novembre 2020).
- (2015): Effects of Building Configuration on Seismic Performance of RC Buildings by Pushover Analysis. Dans: Open Journal of Civil Engineering, v. 5, n. 2 ( 2015).
- (2018): Assessment of Rheological and Piezoresistive Properties of Graphene based Cement Composites. Dans: International Journal of Concrete Structures and Materials, v. 12, n. 1 (octobre 2018).
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