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