Scholarship win supports the selection of Digital Twin tools

Amir Mahdiyar MCIOB is a post-doctoral researcher at the Urban Digital Twin Lab at the University of Central Florida (UCF) in the United States. As a PMI Senior Construction Professional (PMI-SCP), Chartered Construction Manager (MCIOB), and Fellow of Higher Education Academy (FHEA), his research and teaching at UCF focus on the adoption and implementation of Digital Twin technology in construction and infrastructure projects.

My Background in Digital Twin

My journey into the world of Digital Twin began in 2021 while I was a senior lecturer at Universiti Sains Malaysia (USM), where I also led the Project Management Programme. During this time, I supervised two PhD students who worked on a Digital Twin-related topic. In addition, I introduced Digital Twin into the syllabus of the “Information and Communication Technology in Project Management” course for graduate students - marking its first inclusion in the curriculum at USM. This growing passion for Digital Twin technology drove me to explore its applications more deeply in the built environment, ultimately leading me to the Urban Digital Twin Lab at UCF. Here, I am committed to advancing the use of Digital Twin technology in construction and infrastructure projects.

The concept of my proposal

My proposal, which won the Construction Innovation & Quality Scholarship 2024, was inspired by statistics indicating that although many contractors believe emerging technologies can enhance productivity, more than half of projects fail to effectively adopt these technologies. The construction industry, lagging behind sectors like manufacturing in technology adoption, faces a higher risk of failure when implementing advanced technologies like Digital Twin. Given the complexity and variety of technologies encompassed by Digital Twin, selecting the right technology and aligning its maturity level with project needs is critical.

One major challenge is the risk of selecting the wrong technology or mismatching its maturity level with the project's requirements. To address this, my research focuses on developing a Decision Support Tool (DST) designed to assist project managers in determining the most suitable and feasible Digital Twin maturity level during front-end planning. This tool will help assess the project's needs and the company's readiness, ensuring that the Digital Twin implementation is both appropriate and effective.

When to use the Decision Support Tool

Considering the high costs associated with Digital Twin technologies - such as Artificial Technology (AI), Machine Learning (ML), and complex data management - the DST will enable stakeholders to make informed decisions about the necessary maturity level of Digital Twin technologies from the project's outset. Its user-friendly interface, designed for Microsoft Excel, ensures accessibility for construction professionals across firms of all sizes. The DST can be customized based on specific company requirements, allowing both small and large construction companies to tailor the tool to their unique needs. This flexibility empowers companies to leverage Digital Twin technology effectively while mitigating associated risks.

Data for this project will be collected from experts in Digital Twin adoption and implementation within the construction sector. If you are an expert in this field and interested in contributing to this project, please email me or CIOB Senior Qualifications Liaison Manager Hassana Ahmed.