Tag: structural health monitoring
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Metamaterials-based structural health monitoring
The aim of this thesis is to explore numerically and experimentally the fundamentals of wave elastodynamics in active, nonlinear, and topological metamaterials, and to identify functional behaviors that can be engineered to realize modules with a well-defined functionality. The implementation, then, consists of a network of modular transducers that can be easily embedded in a host structure and
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Fault Detection and Management in Sensors for Structural Health Monitoring of Railway Bridges
fault detection, permanent monitoring system, Railway bridges, sensors fault, structural health monitoringStructural health monitoring (SHM) of railway bridges heavily relies on data collected from sensor networks (accelerometers, strain gauges, data acquisition systems). However, technical faults in sensors – such as electrical failures, connection errors, drift, or hardware malfunctions – may compromise the reliability of the measurements. In some cases, fault-induced signals can be mistakenly interpreted as
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Realization of a laboratory scaled steel truss bridge
As direct damage application on full-scale bridge structures is unfeasible, scaled laboratory models provide a suitable alternative for the validation of structural health monitoring (SHM) algorithms. Such models enable the introduction of controlled damage with known location and extent. The objective of this thesis is to design and construct a scaled laboratory model of an
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Transfer Learning and Domain Adaptation for Bridge Monitoring
Structural health monitoring of bridges and viaducts is a crucial field to ensure infrastructure safety and durability. However, one of the main challenges is the scarcity of available data for newly monitored structures, which makes it difficult to train reliable machine learning models. Additionally, assessing the health condition of a structure at the beginning of
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Data-Driven Structural Health Monitoring of Railway Bridges through Signal Decomposition Methods
Anomaly detection, Machine Learning, Railway bridges, Signal processing, structural health monitoringContacts: Viviana Giorgi, Gabriele Cazzulani, Claudio Somaschini Structural health monitoring of bridges and viaducts is crucial to ensure safety and operational reliability. A promising approach relies on the analysis of acceleration signals recorded during train passages, which contain valuable information about the structural dynamic state. However, extracting robust diagnostic indicators from such high-frequency signals is challenging due to