AI used to examine construction following earthquakes

AI used to examine construction following earthquakes


The app helps engineers identify structural issues. Photo courtesy: Build Change

The app helps engineers identify structural issues. Photo courtesy: Build Change

A new open source machine learning tool has been developed to help inform quality assurance for construction in emerging nations.

 

Build Change, with support from IBM as part of the Call for Code initiative, created the Intelligent Supervision Assistant for Construction (ISAC-SIMO) tool to feedback on specific construction elements such as masonry walls and reinforced concrete columns.

 

Structural issues

 

The aim is to help engineers identify structural issues in masonry walls or concrete columns, especially in areas affected by disasters.

 

Users can choose a building element check and upload a photo from the site to receive a quick assessment.

 

“ISAC-SIMO has amazing potential to radically improve construction quality and ensure that homes are built or strengthened to a resilient standard, especially in areas affected by earthquakes, windstorms, and climate change,” said Dr Elizabeth Hausler, founder and CEO of Build Change.

 

“We’ve created a foundation from which the open source community can develop and contribute different models to enable this tool to reach its full potential. The Linux Foundation, building on the support of IBM over these past three years, will help us build this community.”

 

The ISAC-SIMO project, hosted by the Linux Foundation, was imagined as a solution to help bridge gaps in technical knowledge that were apparent in the field. It packages important construction quality assurance checks into a mobile app.

“ISAC-SIMO has amazing potential to radically improve construction quality and ensure that homes are built or strengthened to a resilient standard, especially in areas affected by earthquakes, windstorms, and climate change”

The app ensures that workmanship issues can be more easily identified by anyone with a phone, instead of solely relying on technical staff. It does this by comparing user-uploaded images against trained models to assess whether the work done is broadly acceptable (go) or not (no go) along with a specific score.



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