Unsupervised real‐time SHM technique based on novelty indexes. Issue 7 (16th May 2019)

• Record Type: Journal Article
• Authors: de Almeida Cardoso, Rharã; Cury, Alexandre; Barbosa, Flavio; Gentile, Carmelo
• Is Part Of: Structural control and health monitoring. Volume 26:Issue 7(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-16
• Subjects: novelty detection -- real‐time monitoring -- structural health monitoring -- symbolic data analysis -- unsupervised statistical learning; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2364
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: Structural health monitoring programs play an essential role in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, tall buildings, towers, and old historical buildings. Mostly, an SHM process needs to be based on a trustful strategy for detecting structural novelties or abnormal behaviors. Usually, such an approach is complemented with human inspection and structural instrumentation routines, where the latter requires proper hardware equipment and software tools. Recently, many advances were achieved regarding the hardware resources, such as wireless communication, remotely configurable sensors, and other data management devices. On the other hand, the software counterpart still is in its early developments. Several researches are in progress to fill this gap. In this context, this paper presents a novel online SHM identification method suitable to unsupervised real‐time detection of abnormal structural behaviors. The proposed methodology includes the use of an original representation of raw dynamic signals, that is, in situ measured accelerations. To assess the proposed approach, numerical simulations and two experimental applications are studied: a railway viaduct, PK 075+317 in France and an old masonry tower in Italy. The results suggest that the proposed detection indexes are suitable for a wide range of SHM applications.