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.
Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long-term monitoring, such as the Rio–Niterói Bridge in Brazil, the former Z24 Bridge in Switzerland and the Millau Bridge in France. In fact, some structures are continuously monitored to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, that is, natural frequencies, damping rations and mode shapes. Therefore, the development and validation of tools for the automatic modal identification during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters’ estimates. This work proposes a novel methodology to perform, automatically, the modal identification based on the modes’ estimates data generated by any parametric system identification method. To assess the proposed methodology, several tests are conducted using numerically generated signals, as well as experimental data obtained from a simply supported beam and from a motorway bridge.
The last few years have seen the publication of many papers in the field of vibration-based structural health monitoring (SHM). Methods to detect structural anomalies including damage have received much attention, but there is still some scepticism about their use in practice. Part of this can be explained by the lack of robustness and practicality of the proposed algorithms, which are challenged to detect small structural changes quickly and accurately with low rates of false alarms. Several approaches for SHM rely on modal parameters for structural damage identification, so the correct evaluation of these parameters is of paramount importance. This paper presents an enhanced methodology capable of performing automated identification of modal data. To validate such an approach, two applications are studied – one based on numerically generated signals with different noise levels and one based on experimental data acquired during tests on a high-speed train viaduct in France. The results obtained show that the proposed approach is automated, accurate and insensitive to noise.
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