Tendon Anomaly Identification in Prestressed Concrete Beams Based on an Advanced Monitoring MEMS and Data-Driven Detection of Structural Damage.

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The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage. This is an important advantage that offers the opportunity to identify damage with sensors whose position does not have to coincide with the damaged area. The integration of MEMS sensors into structural health monitoring (SHM) systems offers a promising approach to long-term structural maintenance, especially in large-scale infrastructure. This paper presents an anomaly detection technique that analyzes raw sequential data within a statistical framework to detect damage that causes prestress loss of the tendon by exploiting a distributed monitoring system composed of six high-performance MEMS sensors. The proposed system is preliminarily evaluated to identify the frequency of the first mode, and then the proposed methodology is validated on acceleration data collected on a 240 cm beam in three different damage configurations, achieving a high detection accuracy and showing that its output can also evaluate the damage localization.