Data-driven approaches are particularly useful for computer-supported assessment of civil engineering structures (i) if large quantities of sensor data are available, (ii) if the physical characteristics of the structure are complex to model (or even unknown), or (iii) if the computational efforts are to be reduced. This paper, upon a classificational review of machine learning techniques in structural health monitoring, reports on an embedded machine learning approach for decentralized, autonomous sensor fault detection in wireless sensor networks, facilitating reliable and accurate structural health monitoring. Based on decentralized artificial neural networks, the embedded machine learning approach is applied to perform autonomous detection of sensor faults injected in the acceleration response data collected by a prototype structural health monitoring system. As demonstrated through laboratory tests, the results highlight the ability of the embedded machine learning approach to autonomously detect sensor faults in a decentralized manner, thus enhancing the reliability and accuracy of structural health monitoring systems.
