A bio-inspired framework for highly efficient structural health monitoring and vibration analysis

Civil engineering structures are continuously exposed to the risk of
damage whether due to ageing effects, excessive live loads or extreme events,
such as earthquakes, blasts and cyclones. If not readily identified, damage will
inevitably compromise the structural integrity, leading the system to stop
operating and undergo in-depth interventions. The economic and social impacts
associated with such an adverse condition can be significant, therefore effective
methods able to early identify structural vulnerabilities are needed for these
systems to keep meeting the required life-safety standards and avoid the
impairment of their normal function. In this context, vibration-based analysis
approaches play a leading role as they allow to detect structural faults which lie
beneath the surface of the structure by identifying and quantifying anomalous
changes in the system’s inherent vibration characteristics. However, although
the considerable degree of maturity attained within the fields of experimental
vibration analysis (EVA) and structural health monitoring (SHM), several
technical issues still need to be addressed in order to ensure the successful
implementation of these powerful tools for damage identification purposes.
The scope of this paper is to present a bio-inspired framework for optimal
structural health monitoring and vibration analysis. After a critical overview on
current methods and tools, three main sources of bio-inspiration are described
along with the relative algorithms derived for SHM applications. It is shown
how uncovering the general principles behind the functioning of selected biological systems can foster the development of efficient solutions to the technical
conflicts of actual SHM architectures and lead to new sensing paradigms for
optimal network topology and sensors location. A compatibility-matrix is proposed to help compare biological and SHM systems and discriminate desired
from unwanted features. Such a framework will ultimately assist in seeking for
the most suitable nature-inspired solutions for more accurate condition screening
and robust vibration analysis.