AI, ML, sensors, the IoT, and the cloud come together to monitor and maintain bridges.
Exponential global population growth—set to reach 8.5 billion by 2030—coupled with global macrotrends like urbanization and electrification are elevating the need for reliable transportation infrastructure. Increasing numbers of people will be driving heavier vehicles on roads and bridges, increasing the number of maintenance cycles.
Bridges are vital pieces of infrastructure, moving people and goods over water and land more efficiently. Therefore, maintenance and repair of bridges are essential to prevent failure, and sensor technology is well suited to predict when a bridge needs repairing.
Structural health and monitoring of bridges
The American Society of Civil Engineers issued a report citing current statistics on bridge health. Among these are the following: There are 617,000 bridges across the United States, 42% of all bridges are at least 50 years old, 46,154 (or 7.5%) of US bridges are considered structurally deficient, and Americans make 178 million trips daily across bridges in poor condition.
The study indicates the impracticality of completely refurbishing the bridges. But among the innovative solutions to failing bridge health is the use of networks of sensors to monitor the structural well-being and condition of existing bridges to better anticipate failure.
Historically, bridge monitoring has consisted of a prescribed frequency of visual inspections. The challenge with this approach is that when an inspector visually identifies a problem, the bridge has likely already undergone significant damage. To mitigate this, engineers implemented structural health monitoring (SHM), adding sensors and enhanced technology such as artificial intelligence (AI), machine learning (ML), and remote monitoring.
To keep bridges operational, civil engineers employ sensors to collect data in two main areas: failure repairs and preventative maintenance. In addition, AI and ML have accelerated the move toward continuous monitoring to collect the massive amounts of data required for those technologies.
Parameters that indicate bridge health
Several conditions help engineers understand a bridge’s structural health. These include the following:
External loading from wind and traffic. Wind affects the bridge cables’ vibration profile, while traffic tonnage can identify pressure cycles that negatively impact the bridge’s structural integrity.
Structural deformation like stress, strain, and material displacement. Stress is a measure of force per unit area, and strain quantifies material deformation resulting from stress. Displacement measures how far a material has moved from its initial state. Monitoring changes in these parameters can indicate the likelihood of failure.
Environmental effects like temperature, wind, humidity, oxidation, and local salinity. Rising temperatures modify metal properties and durability, and excessive salinity or oxidation can degrade or corrode the material faster than the design predicted.
Bridge monitoring sensors
Several sensor types can measure the parameters presented above as follows:
Accelerometers measure the vibration from the element over which they are placed and then convert that signal into measurable electrical output. An accelerometer can measure three-axis bridge tilt induced by a wide range of persistent environmental conditions, including wind and traffic. Vibration-based monitoring as part of ongoing data collection can paint a clear picture of the overall structural integrity of the bridge. These sensors can also assess the effect of a dramatic change in operating conditions (e.g., an earthquake) on bridge function.
Strain gauges can monitor steel, reinforced concrete, and other materials on bridges. A common type of strain gauge has a metal foil pattern that, when under strain, deforms and changes its electrical resistance. Measuring this change gives users a measurement of the stress on the structures.
Inclinometers measure how much a bridge has moved beyond its initial designed and constructed mode. An inclinometer functions much like a spirit level with a bubble that indicates how off-center the element is.
Fiber optic sensors measure a variety of parameters, such as temperature, pressure, and humidity. The transmission of light and its subsequent behavior as it passes through materials is the foundation for estimating these parameters. In addition, different materials have unique light refraction and reflection properties.
Force sensors and load cells enable measurement of vehicle weight on bridges.
Crackmeters and jointmeters have a sliding element that measures the gradual growth of a fracture, which allows engineers to measure the movements of cracks and joints.
The future: IoT and cloud-based solutions
Bridge engineering is moving toward a more technology-driven approach to routine maintenance through AI and internet of things (IoT) technologies. Engineers can use AI to accurately predict breakdowns or identify abnormal behavior patterns, leading to a revised approach toward collecting and processing sensor data.
Bridge engineers can create a digital twin from all the data inputs to understand how the different parameters affect each other and how modifying one parameter could affect the others. Sensors can also leverage harvested, stored, or
solar energy to conserve energy. New construction can integrate sensors into bridge materials for a more seamless and aesthetic product.
One of the advantages of such a continuous sensor solution based on IoT technology is that problems are spotted early and can be fixed at less expense and disruption than if they were to lead to breakdowns and potential loss of life. Cost savings, ability to manage regulatory compliance, and extended asset life also appeal to governments on a tight budget. Another advantage is that continuous monitoring can function remotely, eliminating the need for expensive on-site support.
As evidenced by the 2021 US Infrastructure Investment and Jobs Act, upgrading infrastructure maintenance is a high priority for governments at every level. Advanced sensors can deliver essential data to optimize investments for current repairs and predictive maintenance monitoring.
Historically, bridge monitoring was a systematic and visual process, often leading to reactive measures to address uncovered issues. Today, sensor technology can measure a wide range of factors to predict where failures will happen in the future through near-continuous data collection and analysis. In addition, sensors can help to alleviate critical constraints like the pressure to deliver sustainability targets and maintain infrastructure budgets. By means of technologies such as machine learning and artificial intelligence, coupled with a wide array of sensors, bridge engineers are increasingly adopting cloud-based, IoT-driven monitoring solutions.