Muhammad Fawad 
- (2024): Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches. In: Case Studies in Construction Materials, v. 20 (Juli 2024).
- (2024): Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models. In: Case Studies in Construction Materials, v. 20 (Juli 2024).
- (2024): Numerical study on the design performance of wedge-type precast horizontal wall-slab joint for vertical load transfer. In: Structures, v. 60 (Februar 2024).
- (2024): Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms. In: Case Studies in Construction Materials, v. 20 (Juli 2024).
- (2024): Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms. In: Case Studies in Construction Materials, v. 20 (Juli 2024).
- (2024): Forecasting the strength of nanocomposite concrete containing carbon nanotubes by interpretable machine learning approaches with graphical user interface. In: Structures, v. 59 (Januar 2024).
- (2023): Forecasting the Strength Characteristics of Concrete incorporating Waste Foundry Sand using advance machine algorithms including deep learning. In: Case Studies in Construction Materials, v. 19 (Dezember 2023).
- (2023): Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures. In: Case Studies in Construction Materials, v. 19 (Dezember 2023).
- (2023): A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete. In: Case Studies in Construction Materials, v. 18 (Juli 2023).
- (2019): Effect of near and far Field Earthquakes on performance of various base isolation systems. In: Procedia Structural Integrity, v. 18 ( 2019).
- (2019): Retrofitting of damaged reinforced concrete bridge structure. In: Procedia Structural Integrity, v. 18 ( 2019).
Structurae kooperiert mit
