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Artificial intelligence is used to inspect construction in the aftermath of earthquakes




The app helps engineers identify structural problems. Image Courtesy: Building Change

A new open source machine learning tool has been developed to help ensure build quality in emerging countries.

The change, with support from IBM as part of the Call for Code initiative, has created the Intelligent Construction Supervision Assistant (ISAC-SIMO) tool to annotate specific building elements such as masonry walls and reinforced concrete columns.

structural issues

The goal is to help engineers identify structural problems in masonry walls or concrete columns, especially in disaster-affected areas.

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

“ISAC-SIMO has amazing potential to radically improve build quality and ensure homes are built or strengthened to flexible standards, particularly in areas affected by earthquakes, storms and climate change,” said Dr. Elisabeth Hausler, Founder and CEO of Build Change. .

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

The ISAC-SIMO project, hosted by the Linux Foundation, was envisioned as a solution to help fill gaps in technical knowledge that were evident in the field. It compiles important build quality assurance checks into a mobile app.

“ISAC-SIMO has the amazing potential to radically improve construction quality and ensure homes are built or strengthened to flexible standards, especially in areas affected by earthquakes, storms and climate change”

The app ensures that workmanship issues can be more easily identified by anyone with a phone, rather than relying solely on technical staff. It does this by comparing user-uploaded images with trained models to assess whether the work done is broadly acceptable (go) or not (not go) along with a certain degree.

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