Professor Asaad Faramarzi PhD, FHEA

Dr Asaad Faramarzi

Department of Civil Engineering
Head of Civil Engineering
Professor of Civil and Geotechnical Engineering

Contact details

Telephone
+44 (0) 121 414 5050
Email
a.faramarzi@bham.ac.uk
Twitter
@AsaadFaram
View my research portal
Address
School of Engineering
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Professor Faramarzi is a Professor of Civil and Geotechnical Engineering in the School of Engineering. His main research interests are application of computational modelling, and machine learning in geotechnical infrastructure modelling. He also has interest in geophysical sensing, ground engineering, experimental modelling and energy geotechnics. He has published over 100 research papers in prestigious journals and international conferences.

Qualifications

  • FHEA, 2014
  • PGCert in HE, 2014
  • PhD in Geotechnical Engineering, 2011
  • MSc in Structural Engineering, 2006
  • BSc (Eng) in Civil Engineering, 2004

Teaching

Professor Faramarzi leads the teaching on Numerical Approaches and Design in Geotechnics and contributes to Geotechnical Engineering 3. He is also involved in supervising final year projects and MSc dissertations.

Postgraduate supervision

Prof Asaad Faramarzi's main research interest is in computational modelling applied to geotechnical engineering problems. He is interested in developing novel numerical procedures for sensing techniques to capture buried infrastructure-ground interaction, and locating and condition assessment of underground features. He also has interest in poroelasticity, inter-particle interaction of granular assemblies and novel foundations to support offshore structures.

Prof Faramarzi's current research includes:

Inferring data from quantum technology sensors
Finite element modelling of geotechnical problems
Simulation and prediction of inter-particle interaction of granular assemblies
Application of machine learning in geotechnical and civil engineering problems
Offshore geotechnics

Research

Research interests:

  • Computational Geotechnics
  • Application of machine learning in geotechnical and civil engineering problems
  • Condition assessment of buried infrastructure and their supporting ground
  • Inferring data from sensors
  • Quantum Technology (QT) gravity sensors

 

Most recent funded research projects:

  • Quantum Technology - Mapping and map integration for Buried Assets (QT-MIBA), PI - ISCF
  • Ground and Underground Infrastructure Damage Evaluation (GUIDE), PI - EPSRC
  • QVision2, PI - EPSRC
  • iFEEL, PI - EPSRC
  • FRP Shear Strengthening of Damaged Concrete Beams Subjected to Fatigue Loading, CI - British Council
  • UK National Quantum Technology Hub in Sensing and Timing, CI - EPSRC
  • GUIDE  (Ground and Underground Infrastructure Damage Evaluation) - EPSRC

Other activities

  • Chair, 25th UKACM Conference on Computational Mechanics, University of Birmingham, April 2017
  • Guest Editor: Environmental Geotechnics (ICE), special issue: Reservoir engineering: geo-environmental issues
  • Member of the Scientific Committee of the 22nd ACME Conference on Computational Mechanics, 2014.
  • Member of the British Geotechnical Association (BGA), UK.

Publications

Recent publications

Article

Chen, K, Eskandari Torbaghan, M, Thom, N, Garcia-Hernández, A, Faramarzi, A & Chapman, D 2024, 'A Machine Learning based approach to predict road rutting considering uncertainty', Case Studies in Construction Materials, vol. 20, e03186. https://doi.org/10.1016/j.cscm.2024.e03186

Ye, Z, Lovell, L, Faramarzi, A & Ninic, J 2024, 'Sam-based instance segmentation models for the automation of structural damage detection', Advanced Engineering Informatics, vol. 62, no. Part C, 102826. https://doi.org/10.1016/j.aei.2024.102826

Tafreshi Moghaddas, SN, Sarabadani, A, Rahimi, M, Amiri, A, Dawson, A & Faramarzi, A 2023, 'Geocell-reinforced bed anchored with additional vertical elements under repeated loading', Transportation Geotechnics, vol. 42, 101089. https://doi.org/10.1016/j.trgeo.2023.101089

Mehravar, M, Harireche, O, Faramarzi, A, Rahimzadeh, F, Osman, A & Dirar, S 2023, 'Installation performance of structurally enhanced caissons in sand', Computers and Geotechnics, vol. 159, 105464. https://doi.org/10.1016/j.compgeo.2023.105464

Monzer, A, Faramarzi, A, Yerro, A & Chapman, D 2023, 'MPM Investigation of the Fluidization Initiation and Post-Fluidization Mechanism Around a Pressurized Leaking Pipe', Journal of Geotechnical and Geoenvironmental Engineering - ASCE, vol. 149, no. 11, 04023096. https://doi.org/10.1061/JGGEFK.GTENG-10985

Tafreshi Moghaddas, SN, Khanjani, A, Dawson, A & Faramarzi, A 2023, 'Performance of recycled waste aggregate mixed with crushed glass over a weak subgrade', Construction and Building Materials, vol. 402, 133002. https://doi.org/10.1016/j.conbuildmat.2023.133002

Li, H, Chapman, D, Faramarzi, A & Metje, N 2023, 'The Analysis of the Fracturing Mechanism and Brittleness Characteristics of Anisotropic Shale Based on Finite-Discrete Element Method', International Journal of Rock Mechanics & Mining Sciences. https://doi.org/10.1007/s00603-023-03672-x

Afrasiabi, A, Faramarzi, A, Chapman, D, Keshavarzi, A & Stringfellow, M 2023, 'Toward the optimisation of the Kalman Filter approach in ground penetrating radar application for detection and locating buried utilities', Journal of Applied Geophysics, vol. 219, 105220. https://doi.org/10.1016/j.jappgeo.2023.105220

Dabiri, H, Faramarzi, A, Dall’Asta, A, Tondi, E & Micozzi, F 2022, 'A machine learning-based analysis for predicting fragility curve parameters of buildings', Journal of Building Engineering, vol. 62, 105367. https://doi.org/10.1016/j.jobe.2022.105367

Dabiri, H, Kheyrodin, A & Faramarzi, A 2022, 'Predicting tensile strength of spliced and non-spliced steel bars using machine learning- and regression-based methods', Construction and Building Materials, vol. 325, 126835. https://doi.org/10.1016/j.conbuildmat.2022.126835

Stray, B, Lamb, A, Kaushik, A, Vovrosh, J, Rodgers, A, Winch, J, Hayati, F, Boddice, D, Stabrawa, A, Niggebaum, A, Langlois, M, Lien, Y-H, Lellouch, S, Roshanmanesh, S, Ridley, K, Villiers, GD, Brown, G, Cross, T, Tuckwell, G, Faramarzi, A, Metje, N, Bongs, K & Holynski, M 2022, 'Quantum sensing for gravity cartography', Nature, vol. 602, no. 7898, pp. 590–594. https://doi.org/10.1038/s41586-021-04315-3

Conference article

Izonin, I, Tkachenko, R, Mitoulis, S, Faramarzi, A, Tsmots, I & Mashtalir, D 2024, 'Machine learning for predicting energy efficiency of buildings: a small data approach', Procedia Computer Science, vol. 231, pp. 72-77. https://doi.org/10.1016/j.procs.2023.12.173

Conference contribution

Ye, Z, Lovell, L, Faramarzi, A & Ninic, J 2024, SAM-based Structural Surface Damage Detection. in B Riveiro & P Arias (eds), Proceedings of the 31st International Workshop on Intelligent Computing in Engineering. University of Vigo, pp. 176-185, 31st International Workshop on Intelligent Computing in Engineering, Vigo, Spain, 1/07/24. <https://3dgeoinfoeg-ice.webs.uvigo.es/proceedings>

Faizi, K, Hojjati, A, Faramarzi, A & Dirar, S 2022, Evaluation of seepage flow during installation of suction caisson foundation in homogeneous sand and sand overlaying inclined clay. in A Lemnitzer & AW Stuedlein (eds), Geo-Congress 2022: Deep Foundations, Earth Retention, and Underground Construction. Geotechnical Special Publication, no. 332, American Society of Civil Engineers (ASCE), pp. 309-318, 2022 GeoCongress: State of the Art and Practice in Geotechnical Engineering - Deep Foundations, Earth Retention, and Underground Construction, Charlotte, United States, 20/03/22. https://doi.org/10.1061/9780784484029.031

Preprint

Ye, Z, Lovell, L, Faramarzi, A & Ninić, J 2024 'Sam-Based Instance Segmentation Models for the Automation of Structural Damage Detection' SSRN. https://doi.org/10.2139/ssrn.4750668

View all publications in research portal