Engineering summary
With 56,000 structurally deficient US bridges and the I-35W and FIU collapses as warnings, continuous sensor-based SHM gives engineers the data to intervene before failure.
In short: Proper bridge monitoring means installing a structural health monitoring (SHM) system of accelerometers, potentiometers, inclinometers, strain gauges, thermocouples and weather stations that reports a bridge's real-time condition and flags changes in performance before they become failures. With about 56,000 structurally deficient US bridges carrying 188 million trips a day, and collapses such as I-35W in Minneapolis and the FIU pedestrian bridge, continuous SHM gives officials the data to intervene early.
There are about 56,000 structurally deficient bridges in the U.S., and these bridges accommodate on average 188 million trips each day according to data from the Federal Highway Administration.
The nation’s transportation infrastructure is aging. More than 200,000 bridges are now more than 50 years old, and many are approaching the end of their design life. Although the seismic construction requirements are aimed to protect the lives of those crossing bridges. The number of bridges that are in such poor condition as to be considered structurally deficient is increasing and posing potential risk.
Fourteen years ago, the Interstate 35 bridge over the Mississippi River in downtown Minneapolis collapsed. The cars, trucks and even a school bus were driving in bumper-to-bumper traffic across in the evening rush hour. Bridge’s failure plummeted them into the water and onto the rocky river banks. This disaster left a death toll of thirteen and injured 145. The officials were warned that the bridge was structurally deficient due to significant corrosion in its bearings. A federal inspection also rated the bridge structurally deficient, giving it a 50 on a scale of 100 for structural stability.
In 2018, another horrific bridge collapse occurred in the Florida International University campus in Miami. The failed pedestrian bridge killed six and injured eight people. The bridge was under construction and the errors in the design overestimated how much stress the structure could take.
The question arose about the cause of the collapse of bridges and whether they could have been prevented.
Emergent technologies in sensing, artificial intelligence (AI), and cloud computing are now remedying engineers to construct stronger bridges and also improve bridge maintenance for longer life-span. To provide continuous feedback on the bridge’s structural conditions, sensors supporting structural health monitoring (SHM) systems are being installed into both new and existing bridges.
A robust SHM system, including various sensors and data analytics to monitor the bridge’s real-time integrity, can provide officials and engineers with the knowledge and “peace of mind” that the bridge is performing as expected, and the ability to detect a change in its performance. This knowledge and ability are critical because they are directly responsible for the consequences of failure. To prevent catastrophic collapses, especially the bridges that require significant maintenance, rehabilitation, or replacement can significantly benefit from the SHM system to monitor its elements founds to be in poor condition due to deterioration or damage.
At QuakeLogic, we provide the most comprehensive SHM system for bridges. We are the only company with a cloud-based, AI-powered technology platform performing autonomous structural assessments using sensor data. Our platform sends rapid notifications with the level of shaking intensity in case of an earthquake and whether the bridge’s integrity is compromised. This system can not only monitor for earthquakes but also utilize data from various sensors such as accelerometers, potentiometers, inclinometers, strain gauges, thermocouples, and weather stations. Our platform sends meaningful and easy-to-understand information. This timely and critical information helps the bridge officials and engineers to give informed decisions and plan their responses appropriately.
Our structural health monitoring platform response matches for the first time the timing of the earthquake impact, which was impossible before. This platform can provide timely information that is needed for an understanding of the performance of a bridge and address problems earlier to improve public safety.
Key Takeaways
- About 56,000 US bridges are structurally deficient and carry roughly 188 million trips per day, according to FHWA data.
- More than 200,000 US bridges are over 50 years old and approaching the end of their design life.
- The I-35W collapse in Minneapolis killed 13 and injured 145 after inspectors rated the bridge 50 out of 100 for structural stability.
- The 2018 FIU pedestrian bridge collapse in Miami killed six; design errors overestimated the stress the structure could take.
- SHM systems combine accelerometers, potentiometers, inclinometers, strain gauges, thermocouples and weather stations with cloud AI analytics.
Frequently Asked Questions
Bridge structural health monitoring (SHM) installs sensors on a new or existing bridge and analyzes their data continuously to report the bridge's real-time integrity. Typical sensors are accelerometers, potentiometers, inclinometers, strain gauges, thermocouples and weather stations. The system confirms the bridge is performing as expected and detects changes in performance so engineers can intervene before deterioration leads to failure.
According to Federal Highway Administration data cited in the article, about 56,000 US bridges are structurally deficient, and together they carry an average of 188 million trips every day. More than 200,000 bridges are over 50 years old, so the number in poor condition is rising as the transportation infrastructure approaches the end of its design life.
Both collapses had warning signs. The I-35W bridge in Minneapolis was rated structurally deficient with a 50 out of 100 stability score because of significant bearing corrosion before it fell, killing 13 and injuring 145. The FIU pedestrian bridge in Miami failed in 2018 after design errors overestimated its capacity. Continuous SHM data could have exposed abnormal behavior and prompted earlier intervention.
QuakeLogic's cloud-based, AI-powered platform performs autonomous structural assessments from sensor data. During an earthquake it sends rapid notifications with the level of shaking intensity and an indication of whether the bridge's integrity is compromised, timed with the event itself. Officials get plain-language information to decide whether to close, inspect or reopen the bridge.
A comprehensive bridge monitoring system uses accelerometers for vibration and earthquake response, potentiometers for displacement, inclinometers for tilt, strain gauges for stress in members, thermocouples for temperature effects and weather stations for wind and environmental loads. Combining these data streams in a cloud platform allows the structure's behavior to be assessed continuously rather than only during periodic inspections.
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Topic cluster
Related engineering knowledge areas
- Structural Health MonitoringMonitoring for bridges, buildings, dams, tunnels, industrial facilities, and resilient infrastructure.
- Earthquake Early WarningOn-site detection, alerting workflows, seismic switches, and critical infrastructure warning systems.
- Seismic SensorsSeismometers, accelerometers, geophones, sensor selection, calibration, and field deployment.
- Infrasound MonitoringLow-frequency acoustic sensing for environmental noise, blast, UAV, volcano, and defense applications.
Definitions and references
Terms, standards, and source cues
- SHM: related to Structural Health Monitoring in this QuakeLogic knowledge cluster.
- damage detection: related to Structural Health Monitoring in this QuakeLogic knowledge cluster.
- earthquake early warning: related to Earthquake Early Warning in this QuakeLogic knowledge cluster.
- seismic switch: related to Earthquake Early Warning in this QuakeLogic knowledge cluster.
- seismometers: related to Seismic Sensors in this QuakeLogic knowledge cluster.
- accelerometers: related to Seismic Sensors in this QuakeLogic knowledge cluster.
- infrasound sensors: related to Infrasound Monitoring in this QuakeLogic knowledge cluster.
- low-frequency noise: related to Infrasound Monitoring in this QuakeLogic knowledge cluster.
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