Public Summary Inga Maria Giorgadze

From Academic Vision to Real-World Impact in Dutch Infrastructure 

Before starting this program, most of my experience was in academic topics. From studying case studies in papers, I had created an idealized image of Dutch infrastructure as highly innovative—a model for other countries. This vision intrigued me and motivated me to pursue this project.

However, coming into contact with the actual industry and the real-world context, this idealized image began to change. What I encountered were fragmented efforts, rigid organizations, and a general reluctance to take bold steps. After conducting numerous interviews with professionals in my domain, I realized that people had limited digital understanding and implementation capacity, and that the state of the art was not as advanced as I had read in self-praising academic papers. While some might feel discouraged by this reality, it only strengthened my passion and motivation for the project, as it presented a genuine opportunity to make a difference.

I set aside my academic ego in order to think clearly about practical solutions: How could I create a method that could be immediately adopted with existing capacities? How could I use existing tools, knowledge, and processes to make an impact? I managed to leverage the simplest free software to design a logical process for linking data using basic commands and existing geospatial analysis functions. In practice, anyone—regardless of digital literacy—can implement a pavement digital twin using the method I developed and any GIS software available.

In this way, I debunked the myth that implementing digital twins requires sophisticated digital tools. Instead of focusing on producing an impactful paper, I focused on developing an impactful design process. My project results have been wellreceived by many road authorities (provinces, RWS, and municipalities), and I proudly continue to work on developing and expanding the application of my data-linking method. Currently, we are applying machine learning to structured datasets to create a prediction model for asphalt pavement degradation. With investment from a few stakeholders within the sector (road owners and contractors), we are building a proof of concept for this damage prediction model, which could serve as a significant validation of the impact my project has had on the industry.

Reflecting on my choices, I am deeply satisfied that I remained pragmatic in addressing the challenges I encountered. I am immensely proud that this journey continues to unfold, and I am gratified to still see not only the tangible results of my efforts but also the promising prospects for further impact. After two years of working as a professional in my field, seeing the practical application of my methods and their growing adoption across the industry continues to fuel my passion and commitment to driving meaningful change.