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IoT and digital twins drive bridge and dam safety in real-time | IoT Now News & Reports


Dams and bridges have demanding working conditions. They’re under a lot of stress, managing insurmountable amounts of pressure…and they can’t take vacations. As many dams and bridges worldwide are nearing the end of their service lives, IoT and digital twins can come to the aid of engineers who are tasked with monitoring the condition of these vital structures, helping them to work smarter using real-time data analysis and to keep communities safer.

No matter where in the world a dam or bridge is located, the built infrastructure is subjected to the universal laws of physics and varying environmental impacts that cause erosion, rust and spauling, among other effects. Through storms, extreme temperatures, high winds, and even seismic shifts, dams and bridges must effectively endure forces of nature as well as the human impacts of vehicles and pollution.

As climate change leads to increased frequency of extreme weather events that elevate risk factors (for example, floods), engineers who are entrusted with monitoring infrastructure need access to the most recent data to understand how these structures are performing in conditions that were not consider in the original design.

According to a 2021 United Nations report, many aging dams around the world were designed using historical hydrological data that may no longer be relevant or accurate to determine predictability and safety in the age of climate change. For most bridges, critical maintenance decisions are still based on statistical data stemming from the original engineering design of the structure, with most bridges engineered for a lifespan of 50 to 100 years.

Engineers and operators of dams and bridges are addressing these challenges by adopting IoT-powered digital twin solutions to gain insights and keep data evergreen. By using digital twins to visualize and analyze data from a combination of real-time and historical data sources, they can monitor asset performance, plan maintenance, and assess risk with a greater degree of accuracy over time.

Achieving real-time data in a single view

Remote, automated, and continuous monitoring is ideal for infrastructure assets such as dams and bridges who are more often than not located in remote locations. However, a complete plug-and-play monitoring solution may be challenging to implement due to compatibility issues among sensors from different manufacturers. Remotely monitored sensors and equipment may use different data transmission standards; definitions, vocabulary, data storage, and varying exchange formats; and the software designed for processing and analyzing sensor data might not be universally compatible with the wide range of sensors available for monitoring these assets.. In addition, those responsible for the condition or environmental monitoring might not have the necessary expertise to ensure that everything works together seamlessly.  

In fact, in a recent study conducted by ThoughtLab – a global research firm – a lack of knowledge around telemetry was cited as the number one challenge by 78% of all respondents. To vault these hurdles, dam and bridge owners often work closely with ecosystem partners and consultants and use available sensors that correlate well with hard-to-sense parameters. They are able to put together a range of equipment from different manufacturers and ensure that the telemetry from these disparate sensors can be transmitted to – and correctly interpreted by – a company’s monitoring systems and software.

Risk-informed decision-making

Some of these monitoring systems and software might, in many cases, include a digital twin environment; a valuable tool for reducing human error and uncertainties and protecting surrounding communities from the dangerous outcomes of infrastructure failures. By leveraging this new infrastructure intelligence and seamlessly integrating field-critical data from IoT sensors, drone-based photogrammetry, AI and analytics, as well as historical inspection records, organizations can gain insights and generate a single, up-to-date view of asset health, leading to enhanced engagement, communication, and collaboration for improved decision-making.

In California, Yuba Water Agency is leading the way, using digital twins to improve risk assessment and speed up response time to adverse events at the New Bullards Bar Dam. Standing 645-feet-tall, the concrete arch dam on the North Yuba River is the second tallest in California and the fifth tallest in the United States. It was built over 50 years ago to reduce flood risk, generate clean hydropower, and ensure a reliable water supply for the people of Yuba County. 

Historically, collecting condition data on the dam has been a time-consuming process, conducted a couple of times per year, that exposed survey teams to remote and difficult-to-access terrain, and a fall hazard that necessitated additional training in rope access and fall protection in high-risk locations. These safety hazards limited survey points and prevented monitoring of the dam in its entirety. And the excessive length of time it took to capture and analyze the data limited effectiveness and confidence in decision-making. 

Today, operators of the New Bullards Bar Dam are using IoT devices to remotely collect condition monitoring data over the entire face of the dam, including measuring the behavior of the dam structure as time passes and as the structure is exposed to different environmental conditions. Drone imagery helps create a 3D reality mesh to give context to the IoT data and represent the dam in a digital twin view. AI-based crack detection is then applied to the reality mesh  to inform proactive dam maintenance. Condition data can trigger automated alerts in the digital twin based on predetermined early warning thresholds. Such automation improves workflows and is known to improve data quality for reporting consequence analysis to stakeholders.

Safer Inspections, safer infrastructure

Maintaining safe infrastructure for communities requires regular inspections, and inspecting dams and bridges can be dangerous work. When drone images and sensor data inform the digital twin, the result is reality modeling that provides a much safer alternative to using a human inspection crew to scale dangerous and hard-to-reach areas and during extreme weather. The real-time insights gained from IoT data also allows maintenance to be planned on a proactive basis.

The Highland Cable Bridge in Denver, Colorado, is one of three pedestrian bridges that connects the Highland neighborhood to downtown Denver. It is 320 feet long and features a unique design that produces more movements than bridges of similar sizes. The design also makes manual inspections and maintenance a challenge.

To address this unique condition-monitoring challenge, the city decided to create a 3D model of the bridge based on design plans and high-resolution images captured by drones. It then installed 15 low-cost wireless sensors with accelerometers and tilt meters along the length of the bridge to comprehensively capture the dynamic movement and vibrations of the structure. Sensors send data back to the digital twin where the virtual 3D model provides insights to the team and alerts the city if measurements are significantly outside of the normal range. 

Putting AI to work

Condition monitoring systems can generate extensive amounts of data that must be reviewed and analyzed to provide insights and accurate reporting. Artificial intelligence and machine learning serve as integral tools to identify changes in dam and bridge behavior outside of normal risk conditions. The combination of digital twins, AI, and IoT technology also is helping to reduce costs amid a shortage of skilled workers.

Looking ahead, actionable insights from the field will continue to play a larger role in the sustainability and resilience of existing and new infrastructure, including that of dams and bridges as well as tunnels, roads and railways, and civil construction sites. Real-time IoT data from sensors coupled with drone-captured 3D models and AI implemented in digital twins will be at the forefront – optimizing sustainable practices like energy efficiency and waste reduction, minimizing environmental impact, and increasing safety.

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