Theoretical and Applied Engineering

Shock and Vibration (pdf)

Machine Learning Techniques for Structural Health Monitoring

In recent years, there is an increased need for early detection of structural faults or damage, what has been referred as structural health monitoring (SHM). Structural health monitoring involves the use of a sensor network to collect data from the monitored structure. The amount of data collected from a given structure is often complex and large. Thus, data analytics has become an important process to extract valuable information from the measurements for decision making with respect to diagnosis of the structural condition and the prognosis of structural damage.

This special issue is aimed to exploit the advances in applying Machine Learning Techniques for Structural Health Monitoring. We invite researchers to contribute with original research articles that include new theoretical approaches, numerical simulations or experimental studies of applying Advanced Signal Processing and Computational Intelligence Techniques for Structural Health Monitoring. We also welcome review articles summarizing the current state of the art.