Instantaneous identification of densely instrumented structures using line topology sensor networks

Abstract

Summary In this paper, a new strategy for vibration‐based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star‐topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real‐time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures.

Crack identification using electrical impedance tomography and transfer learning

“…This strategy was proposed in previous studies (Quqa et al., 2022; Quqa et al, 2023) and will be used here to represent the reference performance of a DNN‐based method to solve the inverse ERT problem. This DNN will be referred to as “preliminary” DNN hereafter, as it is trained on synthetic data alone.…”

“…Previous studies showed that, in some cases, synthetic data alone could be enough to train neural networks for the identification of conductivity changes due to damage or strain variations when the sensing body is originally undamaged (Quqa et al., 2022). Nevertheless, while early damage might be identified with good accuracy (Quqa et al, 2023), the performance of neural networks typically degrades with damage severity (L. Chen, Gallet, et al., 2022; Hallaji et al., 2014; Seppänen et al., 2017). However, not all surface disconnections are necessarily related to structural damage.…”

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges

“…For example, WSS can easily be implemented to build sensor networks that meet the requirements of sudden event monitoring with minimal power consumption [ 9 ]. They can be exploited to handle system identification for densely instrumented infrastructures and structures [ 10 ]. They offer versatile networks that can be interfaced with a variety of sensors (accelerometers, strain gauges, thermistors, LIDAR, etc.)…”