Real-time monitoring of bridges with digital twins is helping ensure structural integrity, extend life, and save money.
The bridges constructed over the past century are some of the most impressive structures on the planet, and they symbolize an increasingly connected world that seeks to overcome all barriers. Advancements in building materials for bridges — particularly the combination of concrete and steel cable — have simplified transportation for travelers everywhere.
But the clock is ticking on millions of aging bridges all around the world.
In the U.S. alone, more than 54,000 of the over 600,000 bridges are rated “structurally deficient.” There are several factors that cause bridge deterioration, but for reinforced-concrete structures, the primary causes are corrosion and increased loads.
Countries in Europe are also striking alarm that without proper monitoring and maintenance catastrophic failures like that of the Morandi Bridge in Genoa, Italy in August 2018 can be expected. That tragedy, which cost 43 lives, was a wake-up call for the many countries where road maintenance has been sorely neglected. The problem is expected to worsen with the expanding development in BRIC countries (Brazil, Russia, India, and China) and other growth markets.
Engineering Intelligence Against the Collapse
While the repair and replacement of structurally deficient bridges will require much time and money, the public sector is looking for affordable and effective monitoring methods to ensure bridge safety.
At SAP’s Engineering Center of Excellence, based in Trondheim, Norway, a team of software engineers and civil engineers have joined forces to develop a new real-time monitoring system for bridges based on the SAP Predictive Engineering Insights solution. The wealth of data collected will deliver greater insight into bridges so they can be properly maintained and safely operated.
With the permission of the Norwegian Public Roads Administration (NPRA), SAP is monitoring the Stavå bridge on the E6 between Oslo and Trondheim. With its elegant arches spanning a deep, wooded ravine, the bridge can only be described as picturesque. But for the NPRA, the reinforced-concrete structure along Norway’s main north-south artery is a constant cause for concern. Dating back to World War II, it was designed for 20-ton freight loads, not the 60-ton loads carried today. For safety reasons, large vehicles can only cross at reduced speed in one direction at a time.
Simulation-Based Digital Twins for Bridges
SAP engineers created a digital model that simulates the dynamics of the structure down to the materials level. The result is a “digital twin” that delivers insight into the bridge’s behavior in real time and can provide ample warning of structural failures.
Here is how the Norwegian prototype works: Vehicles passing over the bridge create vibrations, which are picked up by sensors attached to the structure. Their signals are sent to the cloud software solution SAP Predictive Engineering Insights, where the bridge model — the digital twin — resides. The NPRA would be alerted to abnormalities and know better where to focus inspection and maintenance resources.
Extending Bridge Life
Arild Christensen, who oversees bridges and ferry ports for NPRA in central Norway, believes that real-time monitoring can extend the useful life of bridges.
“The technology may have large economic importance, as one can maximize the bridge lifetime of and start renovations at the optimal time,” explains Christensen. “If the method can help postpone the replacement of a bridge by 10 years, it will save the authorities large amounts of money.”
The technology for real-time monitoring is more reliable than current bridge monitoring techniques that are based on brief periodic sampling, and is already being applied in multiple industry scenarios.
Predicting Failure, but Also Success of Repairs
“We have come far in understanding what the data from the Stavå bridge is telling us about its structural soundness and how this could be applied to other bridge structures to predict possible failure modes,” says Martin Hasle, head of the SAP Predictive Engineering Insights product delivery team in Trondheim.
“For example, we are now able to estimate the traffic load over a given period and its development,” he says. “This information about the structural loading is important to understand the risks of fatigue.” Whenever a truck from ASKO, Norway’s largest grocery wholesaler, passes over the bridge, Hasle’s team automatically receives the vehicle weight and the time of passing. “By knowing the exact weight of the freight loads passing over the bridge, we are able to calibrate the digital twin’s finite element model to the bridge so as to more precisely assess its physical state and level of wear and tear.
The NPRA also wants to apply SAP Predictive Engineering Insights to predict the success of planned reinforcements to the bridge structure. Repairs to one part of the bridge could result in a shift in the load of other parts, with possible negative consequences.
Digital Twins as Time Machines
It is this ability of SAP Predictive Engineering Insights to review the past and peer into the future that makes digital twin technology attractive to asset managers. According to industry analyst IDC, this “time machine” capability is one of the primary reasons why manufacturers are interested in using digital twins.
“Digital twins will enable manufacturers and asset owners to rewind to see what happened, play to see what is happening now in real time, and fast-forward using simulation to see what will happen given a set of factors,” writes Jeffrey Hojlo, program director, Product Innovation Strategies at IDC. “The net result of this mission control approach is better operations and continuous improvement of products, production, and assets.”*
Learning from Past Bridge Failures to Prevent Future Ones
Current methods for bridge monitoring are based on the past failures of bridges. Using available data from the collapse of the I35 bridge over the Mississippi River in 2007, the team from SAP is creating a digital model to simulate failure and therefore establish how it could have been predicted using a digital twin.
“We place virtual sensors on and implement the failure that caused the collapse of the bridge,” explains Hasle. “If you can find the correlations then you can predict failures before they happen.”
Marit Reiso, senior project manager for SAP Predictive Engineering Insights, contributed to this story.
*Digital Twins for Products, Assets, and Ecosystems, IDC PlanScape, Doc # US43134418, April 2018