In 2016, a 6.4 magnitude earthquake rocked Taiwan. Within minutes, Arup’s Hazard Owl was able to asses 40 nearby buildings and provide warnings and recommendations to affected building owners.
We can’t prevent these types of events but we can reduce their impact, plan ahead, and recover more quickly. While Arup holds a wealth of knowledge about the buildings we design, the response is still typically manual and reactive in face of bushfires, floods, tsunamis and cyclones. Hazard Owl supports Arup’s role as a domain expert by giving our clients quick advice on potential hazard impacts to their people, buildings, and businesses.
“The technology taps into streams that already exist’ says Tim Mote. “What we’re adding is Arup’s layer of expertise.” Tim has spent the past five years developing the product with Roland Martin, one of our GIS and machine learning experts in LA.
Hazard Owl integrates BIM models into an automated system that monitors structures against hazard feeds. When hazard events do occur, owners receive an SMS or e-mail letting them know how their infrastructure has been affected and whether or not there are any risk mitigating actions that need to be taken in real time.
Hazard Owl accesses project information by plugging directly into infrastructure BIM models and creates a real-time information feed indicating when a hazard has surpassed a structures vulnerability limits. This lets us warn building owners within minutes of any potential impact and provides instant risk mitigation strategies to protect people and assets.
Tim is also excited about the potential for machine learning within Hazard Owl, particularly across portfolios with a number of assets.
“We’re creating a huge database of the performance of all these structures for our clients over the life of the building. 14,000 engineers around the world and thousands of structures and projects all inputting into one knowledge base--wouldn’t that be something.”
“Take for example high risk slopes on the road. There’s often a network of 3,000 and every year a number of those fail because of rainfall. If we can correlate those failings with rainfall events, we can better analyse those triggers and incorporate those insights into future designs.”
As thousands of Arup-designed projects are plugged into the system, any hazard impacts can be monitored using machine learning to deepen our understanding of asset performance. Moreover, by gathering data we can strengthen our buildings into the future, creating a safer, more sustainable, built environment.
While most hazard feeds are geared for individual safety, Hazard Owl is registering these events with a focus on the resilience of buildings and infrastructure. Hazard Owl registers between 5,000 and 10,000 hazards worldwide at any given time. This research projects allows us to respond to events more quickly and to catch incidents we might otherwise have missed.
Hazard Owl is always watching out for us! She taps into the numerous hazard feeds out there and compares the intensity the event – be it ground shaking from an earthquake or rainfall intensity from a storm – with what our clients infrastructure was designed to withstand. This will allows us to provide them with robust, comprehensive advice as to how their building has been affected within minutes of an event.
“Hazard Owl gives Arup the ability to be reactive and resilient in the face of natural hazards, which are constantly changing, due to climate change.”
“It’s not just about reporting damage.” says Tim. “One unexpected learning we’ve found is that the system returns a lot of value just by telling owners that there’s no expected damage to their building.” This improves business continuity and gives people peace of mind.
This story was written by Jeff McAllister, as part of the Research Review. This series is produced by the Arup Australasia Research team; Alex Sinickas, Bree Trevena and Jeff McAllister with contributions from Sheda and Noel Smyth.
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