TALKING

From rethinking urban development with NEOM in Saudia Arabia to reshaping how goods and people are transported via the Gulf Cooperation Council , GCC rail programme , the sheer number and scope of megaprojects highlight the many opportunities to advance infrastructure across the region .

However , much like Europe , North America , and Asia Pacific , which are also experiencing a surge in infrastructure development , projects in the Middle East face similar engineering and skilled labour constraints , delivery delays and overruns , supply chain challenges , and the growing need to meet resiliency and sustainability requirements .

How can infrastructure engineers , construction contractors , and owner-operators across the region address these challenges and improve project delivery and asset performance ?

Over the last year , there has been a lot of discussion about using AI-powered applications and solutions to improve project efficiency , organisational effectiveness , and infrastructure outcomes . Leveraging AI , machine learning , and other intelligent applications presents immense potential for addressing issues across the infrastructure life cycle .

Unfortunately , data is the foundation for AI-powered anything and the infrastructure sector has a large data management hurdle it must clear before the promise of AI can be realised .

Better data

For decades , the infrastructure project lifecycle was linear . Planning , design , procurement , construction , and operations had distinct phases with individual stakeholders , requirements , and discrete handoffs . Each phase typically used different technologies and processes .

This approach created information silos and data loss , resulting in design rework and errors , project delays , and increased costs and risks .

As infrastructure projects and their respective phases have become more interconnected , there is a real need for bringing engineering , information , and operational technology systems and data together .

But the reality is that an abundance of valuable data from these technology systems is trapped in files , models , drawings , and even paper . Unlocking this data is critical to better decision-making across the infrastructure project lifecycle as well as for using AI-powered solutions Instead of generating critical project and asset data in disparate systems , infrastructure engineers , construction contractors , and owner-operators should start producing data layers with open platforms that generate digital twins .

Digital twins

Over the last few years , digital twins have become a hot topic across every sector . What was once considered futuristic eye candy has evolved into a powerful and valuable way to combine and use data from disparate sources and multiple disciplines into a holistic , dynamic representation of infrastructure projects and assets .

In addition to offering a structured way that brings siloed data together , digital twins can unlock data from existing design files , essentially lighting up dark data .

When digital twin capabilities persist across the infrastructure project life cycle , they create workflows that enable engineers to seamlessly conduct design reviews , structural analysis , and calculate carbon footprints .

Digital twin workflows can help construction companies improve the accuracy of quantity take-offs , project scheduling , and more . Just like their value in bringing data together , infrastructure digital twins can connect processes between the different life cycle stages of a project and asset .

Julien Moutte , Chief Technology Officer , Bentley Systems

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