AI Case Study

Build Change assesses quake-hit buildings safety and suggest cost-effective way to repair them using AI

Build Change and its Founder, Elizabeth Hausler, are leveraging machine learning to aid people whose homes have been affected by earthquakes and other natural disaster. The team trained a computer to analyse photos of houses and other data in order to assess the most cost effective and safe way to repair them. In the form of an application that users could download on their phone to receive the assessment, Build Change eliminated the need for lengthy assessment visits to each house in the area of Nepal.

Industry

Public And Social Sector

Ngo

Project Overview

"Artificial intelligence (AI), it turned out, could help, said Elizabeth Hausler, a U.S.-based engineer and builder who works on creating affordable, disaster-resilient housing.

In Nepal, many homes are variations on a standard design - rectangular, multi-story and with similar windows, she said.

“We realized the buildings aren’t that unique,” she told the Skoll World Forum on Social Entrepreneurship in Oxford this week.

Using photos and other data collected on a wide range of Nepalese homes, Hausler and others were able to train computers to analyze photos of quake-hit buildings and then suggest whether they could be repaired safely and cost-effectively.

Property owners could download an app created by Hausler’s team, upload photos of the home in question, and get back a machine-assisted assessment of the best plan for them, said the founder of Build Change, a Denver-based organization that aims to reduce losses from home and school collapses in disasters.

In disaster zones, AI-assisted efforts can help builders get back to work faster, spurring economic recovery, she added.

Hausler’s safe housing group has worked with the World Bank to see if homes on the island of St. Lucia could withstand a hurricane as fierce as that which devastated nearby Dominica in 2017.

They used artificial intelligence and other technologies to examine the island’s housing and make predictions, based on what they knew about Dominica.

They found St. Lucia’s housing stock was similar and likely to collapse in those extreme weather conditions, Hausler said."

Reported Results

"The technology avoided the costs and long delays of having assessment teams visit each home, and helped people rebuild faster and more securely, Hausler said. It is now being used in other countries, including Colombia."

Technology

Function

Background

"When Nepal suffered devastating twin earthquakes in 2015 that killed nearly 9,000 people, the government provided help for families whose homes had collapsed to rebuild.

But tens of thousands of others with damaged homes that were still standing faced a tougher decision: Was it safe to make repairs? Or were they better off building a new, often smaller home at their own cost?"

Benefits

Data

photos and other data on houses and their engineering/architecture