Farham Shahrivar

Project description

My project is called Digital Twin Development for Intelligent Maintenance Management of Civil Infrastructure, and it focuses on intelligent asset management. We’re creating a digital model of a bridge that’s continuously updated with real-time data from the actual bridge. This allows us to monitor the bridge’s structural performance in ways that have never been done before due to the complex and challenging nature of interoperability, data imbalance, and noise.

But it doesn’t stop at just monitoring. The digital twin project also identifies defects and predicts remaining useful life (RUL) of the bridge based on objective structural data. The digital twin project also is scaled up to the network of bridges (with the same specs in terms of dimensions, materials, etc.) to help asset owners prioritise for maintenance. My specific role within this project involves using Artificial Intelligence (AI) to predict RUL of the case study bridge and scale up the DT project to prioritise assets for maintenance. I simulate different defect scenarios and their resulting accumulative degradation, and then use these scenarios to train an AI algorithm.

The goal is to help asset owners, who are dealing with many aging assets and limited budget, by digitising the entire asset management process – from data collection and processing to RUL prediction and maintenance prioritisation.

What led you to undertaking an industry-led research project?

In 2018, while working as a project manager, I became curious about how decisions are made regarding which assets to maintain each financial year. This curiosity sparked my journey into asset management, and I decided to pursue a master’s degree on asset prioritisation for maintenance. The current PhD project I am working on addresses a huge challenge faced by many asset owners or managers: reducing costs. This project aims to address this issue by digitising various steps in asset management.

What have been the highlights of your PhD?

It would be developing a multi-criteria decision-making method that can be applied to any situation characterised by multiple criteria and numerous options relative to those criteria. This method incorporates fuzzy numbers to reduce subjectivity in qualitative decision-making processes, which is pretty cool.

Another significant highlight has been the development of a real-time model for structural monitoring of concrete bridges.

Once you have completed your PhD, what’s next?

Right now, I’m working as a senior project engineer on the Inland Rail project, which is a once in a generation program of infrastructure works which will, when completed, connect regional Australia to domestic and international markets, transforming the way freight is transported around the country. After my PhD, I want to keep advancing in the industry while also taking on a part-time role as a lecturer. My goal is to share my practical, real-world experience with junior STEM students, enriching their education with insights that can help them if they decide to pursue a career in the industry.

How will your research benefit Australia’s concrete ecosystem? 

I approach design during my industry project’s development phase through a comprehensive lens by considering operation and maintenance into the design process. By pursuing this PhD, I’m also gaining additional skills that will help me bridge the gap between industry and academia. I especially want to introduce industry asset owners to research topics they might not be aware of or haven’t implemented yet. This way, I can help bring innovative solutions from academia into real-world practices.