Asaad Faramarzi
- (2022): A machine learning-based analysis for predicting fragility curve parameters of buildings. In: Journal of Building Engineering, v. 62 (Dezember 2022).
- (2022): Predicting tensile strength of spliced and non-spliced steel bars using machine learning- and regression-based methods. In: Construction and Building Materials, v. 325 (März 2022).
- (2021): Effect of transverse and longitudinal reinforcement ratios on the behaviour of RC T-beams shear-strengthened with embedded FRP BARS. In: Composite Structures, v. 262 (April 2021).
- (2020): Monotonic and cyclic lateral load tests on monopod winged caisson foundations in sand. In: Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, v. 173, n. 5 (Oktober 2020).
- (2017): The response of buried pipes to UK standard traffic loading. In: Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, v. 170, n. 1 (Februar 2017).
- (2019): Economical design of buried concrete pipes subjected to UK standard traffic loading. In: Proceedings of the Institution of Civil Engineers - Structures and Buildings, v. 172, n. 2 (Februar 2019).
- (2014): An EPR-based self-learning approach to material modelling. In: Computers & Structures, v. 137 (Juni 2014).
- (2013): Numerical implementation of EPR-based material models in finite element analysis. In: Computers & Structures, v. 118 (März 2013).
- (2018): Development of a novel model to estimate bedding factors to ensure the economic and robust design of rigid pipes under soil loads. In: Tunnelling and Underground Space Technology, v. 71 (Januar 2018).
Structurae kooperiert mit
