Structures close to reaching their design service life, such as a significant proportion of European bridges, require careful and continuous control and supervision to ensure safe usage. As bridges age, they can become increasingly vulnerable to damage and failure because of degradation mechanisms and increasing load requirements. Structural Health Monitoring (SHM) of bridges is the process of using various sensors and measurement techniques to monitor the condition of a bridge and detect any changes or damage that may occur over time. This can include monitoring the structural integrity of the bridge, as well as monitoring the environment around the bridge, such as temperature, humidity, and wind. The data collected by the sensors is then analyzed to identify any potential issues or problems and to help engineers make decisions about maintenance and repairs. SHM can help to increase the safety and longevity of bridges and can also help to reduce the costs associated with maintenance and repairs.
Machine learning has a significant role in vibration-based data-based SHM systems. It can be used to analyze the large amounts of data collected by the sensors, identify patterns, and detect anomalies. Machine learning algorithms can also be used to develop statistical models of a bridge's normal dynamic response, which can then be used to detect when the response deviates from this baseline, indicating a problem or damage. In short, sensors are used to measure the vibrations of a bridge, whereas machine learning algorithms are used to analyze the data and identify patterns and anomalies. This approach can provide a powerful tool for monitoring the condition of bridges and ensuring their safety and integrity over time.