Mohammad Aminpour

Dr. Mohammad Aminpour

Lecturer, Civil Engineering

Details

Open to

  • Masters Research or PhD student supervision

About

Dr Mohammad Aminpour is a University Lecturer in Geo-Environmental Engineering and a geotechnical scientist specialising in computational geomechanics, offshore geotechnics, and data-driven engineering. His research integrates advanced numerical modelling, artificial intelligence, machine learning, and digital technologies to address complex challenges in geotechnical and geo-environmental engineering.

 

He is the Lead of the Intelligent Coastal and Offshore Geotechnics and Infrastructure (ICOGI) Research Group, with research focused on intelligent and computational approaches to coastal and offshore infrastructure, foundation systems, and renewable energy developments.

 

Dr Aminpour is also the Lead Entrepreneur of the Australian Economic Accelerator AEA Ignite project, Cutting Offshore Wind Energy Costs through AI-Enhanced Foundation Design. The project has received AUD $499,860 in AEA Ignite funding to advance AI-enabled approaches for offshore wind foundation assessment and design.

 

His broader expertise includes soil and rock mechanics, deep foundations, soil improvement and remediation, uncertainty assessment, contaminant transport, hydrogeology, and ecohydrology. His computational research applies methods including the Finite Element Method (FEM), Discrete Element Method (DEM), and Lattice Boltzmann Method (LBM).

 

Dr Aminpour also develops data-driven engineering solutions, including machine-learning-based prediction and optimisation frameworks and digital twin approaches for geotechnical and geo-environmental infrastructure. A particular focus of his current research is the application of these technologies to offshore wind energy and foundation engineering.

 

With more than eight years of industry experience, he has contributed to infrastructure projects involving dams, tunnels, oil and gas facilities, wind energy developments, water infrastructure, roads, railways, mines, and power plants.

 

Research Leadership

  • Lead, Intelligent Coastal and Offshore Geotechnics and Infrastructure (ICOGI) Research Group
  • Lead Entrepreneur, AEA Ignite Project: Cutting Offshore Wind Energy Costs through AI-Enhanced Foundation Design (AEA Ignite Funding: AUD $499,860)

Geotechnical & Geo-Environmental Engineering

  • Soil and rock mechanics
  • Offshore and deep foundations
  • Soil improvement and remediation
  • Contaminant transport
  • Computational geomechanics and geotechnical modelling
  • FEM, DEM and LBM numerical methods

AI & Data-Driven Engineering

  • Machine learning for geotechnical prediction and design
  • AI-enhanced engineering optimisation
  • Uncertainty assessment and decision support
  • Digital twins for geotechnical and geo-environmental infrastructure
  • Intelligent foundation design for offshore wind energy

Hydrogeology & Ecohydrology

  • Groundwater analysis and management
  • Groundwater remediation
  • Hydrogeological modelling
  • Ecohydrology and sustainable water-resource management

Industry Experience

  • More than eight years of infrastructure design and construction experience
  • Experience across dams, tunnels, oil and gas facilities, wind energy, water infrastructure, transport, mining and power-generation projects

Community Engagement

  • Dr Aminpour is also the founder of an Australian charitable organisation focused on creating sustainable and meaningful community impact.

Supervisor projects

  • Physics-Informed Data-Driven Modelling of Monopile Foundations for Offshore Wind Turbines
  • 28 Aug 2026
  • Asset Integrity Management of Water Utilities
  • 30 Jul 2025
  • Advanced Deep Learning Approaches for Image-Driven Soil Classification and Property Prediction
  • 26 Feb 2025
  • Pnictogenic Critical Minerals in Secondary Resources: Characterisation and Extraction
  • 14 Nov 2023

Teaching interests

Hydrogeology CIVE1184 (2024 to present)

Geotechnical Engineering 2 CIVE1108 (2021 to present)

Advanced Hydrogeology CIVE1122 (2024)

Ecohydrology OENG1253 (2025)

Research interests

Offshore Geotechnics

Machine Learning Data-Driven Solutions in Geo-Environmental Engineering
Uncertainty / Reliability Assessment in Geotechnical Engineering
Computational Geomechanics (Finite Element Method, Discrete Element Method, Lattice-Boltzmann Method, Couple Multi-physics Methods)
Fluid Transport in Porous Media

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Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Sentient' by Hollie Johnson, Gunaikurnai and Monero Ngarigo.

Learn more about our commitment to Aboriginal and Torres Strait Islander peoples