INVESTIGATION ON DAMAGE SENSITIVE FEATURES FOR OPTIMAL SENSOR NETWORKS BASED ON REAL-SCALE RECORDINGS

摘要

Structural health monitoring (SHM) includes a wide range of methods focusing on the improvement of structural reliability and life cycle management of engineered systems. In the past decades, smart maintenance procedures have been proposed and implemented for a large number of civil structures with strategic and monumental relevance. Nowadays, rapid advances in signal processing and sensor technology allow for the implementation of smart maintenance schemes for conventional buildings and infrastructure. Vibration-based SHM techniques are among the most applied in the structural engineering field and become increasingly popular due to their interdisciplinary nature. In this framework, the establishment of reliable damage-sensitive features (DSF) is crucial for the efficient evaluation of structural health. This work comprises an investigation on the effectiveness of data-driven DSFs for the case of a large scale reinforced concrete benchmark under shaking table excitation. Different damage identification techniques are applied to expose the propagation of structural damage during strong ground motions. A discussion on the hardware and algorithmic requirements for reliable wireless smart sensor networks (WSSNs), capable of capturing the desired DSFs, is also included.