When it comes to construction projects, planning is everything — whether it’s a new hospital or an eight-unit condo.

“Any small mistake at the design phase has a domino effect and is expensive to fix once construction starts,” says Sobhan Kouhestani (MASc 19), a recent graduate of Concordia’s Gina Cody School of Engineering and Computer Science.

It’s an issue that Kouhestani wanted to address.

He created a way to improve the design phase by using process mining, a form of machine learning and artificial intelligence (AI).

“The result is better resource planning, reduction of time-consuming reworks, better monitoring of ongoing projects and improved collaborative design,” says Mazdak Nik-Bakht, assistant professor in the Department of Building, Civil and Environmental Engineering.

Nik-Bakht and Kouhestani recently co-authored a study published in Automation in Construction, a highly respected journal in the field.

Bridging the gap between BIM and process mining

Their story starts at the digital drafting table.

In the design phase, many architects and engineers are using digital authoring software to make building information models (BIM) — “smart” 3D models that contain information like costs, scheduling and materials, such as the type of windows and number of floor tiles.

“It’s called digital twinning,” explains Nik-Bakht, who is also communication and outreach director of Concordia’s new Centre for Innovation in Construction and Infrastructure Engineering and Management (CICIEM).

“With this study, we were able to bridge the gap between BIM and AI by using process mining so that BIM managers get more functionality out of the digital twin. It’s one of a cluster of projects I proposed in 2017 that got funding from NSERC” — the Natural Sciences and Engineering Research Council of Canada.

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