Structural Health Monitoring (SHM) plays a vital role in maintaining the integrity of structures by providing continuous information about damage or anomalies. Vibration-based SHM, which focuses on the dynamic behavior of structures, offers insights into structural conditions through changes in dynamic properties. Among SHM approaches, damage localization is crucial for pinpointing the geometric location of damage. This paper proposes a method for damage localization using Short Time Fourier Transform and a Statistical Interpolation Damage Index. The proposed methodology is applied to a numerical case study involving a finite element beam model and to the S101 benchmark bridge, in Austria, demonstrating its efficacy in damage localization. The study also introduces a multi-level clustering approach to perform damage localization using smart decentralized sensor networks, able to reduce the volume of transmitted data and thereby the energy requirements. Results show promising outcomes in accurately identifying damage locations while minimizing data transmission.
KEYWORDS: Structural health monitoring, Bridges, Sensors, Mode shapes, Prior knowledge, Data transmission, Monte Carlo methods, Engineering, Decision trees, Decision making
Structural Health Monitoring (SHM) can provide valuable information for maintenance-related activities and post-disaster emergency management. However, as with any technological system, SHM systems can be susceptible to errors due to malfunctioning. Therefore, it is essential to assess the potential for malfunctions and the associated costs of maintenance and repair when evaluating the long-term benefits of SHM systems. In the last two decades, sensor validation tools (SVTs) have been proposed to support decisions by isolating and discarding abnormal data. Recently, the authors of this paper have proposed a framework based on the Value of Information (VoI) from Bayesian decision analysis to account for different states of an SHM system and assess the benefit of SVT information. By quantifying the additional value obtained from SVTs, decision-makers can make more informed decisions about investing in these systems. This framework is here demonstrated on a real case study, namely the S101 bridge in Austria, which has been artificially damaged for research purposes. The benefit of collecting SHM and SVT information is quantified by considering a simple decision problem related to the management of the bridge in the aftermath of a damaging event. Overall, the study highlights the potential benefits of using SVTs to improve the reliability of SHM data and inform decision-making in the management of structures.
In this paper two methods of damage localization previously proposed by the authors are combined to smooth the possible drawbacks and boost the advantages each of them. The Modal Interpolation Method (IM), recently proposed, is based on a damage feature defined in terms of the loss of smoothness (that is local increases of curvature) of the modal shapes induced by a local reduction of stiffness. Herein the combination of the IM with the Curvature Evolution Methods (CEM) is proposed. The CEM is based on the use of a Band-Variable Filter able to extract from recorded responses the nonlinear response of one mode of vibration enabling the detection of possible changes of a properly defined damage feature, during a single earthquake. In the CEM the modal curvature is assumed as damage feature. The combination of the two methods CEM and IM is carried out using the Band-Variable Filter to extract the nonlinear response of the structure and assuming as a damage feature the variation of the interpolation error computed at different times during the strong motion.
The validation of the combined approach, named Interpolation Evolution Method (IEM), is carried out on a full scale experimental benchmark tested on the UCSD-NEES shake table.
Non-destructive vibration based methods can be used as diagnostic tool to identify damage in structures. Periodic inspections or permanent monitoring networks of sensors can indicate the emergence of possible damage occurring during the structure lifetime. Several methods have been proposed in literature for damage identification purposes. Some of them allow detecting the existence of damage, others provide information about its location as well. Data driven method are able to localize damage based solely on responses recorded on the structure without the need of a Finite Element model. Many of these methods are based on the detection of irregularities in the deformed shape of the structure: modal or operational shapes have been proposed to this purpose by different authors. The reliability of the methods proposed in literature is often verified on numerical models that, by their nature, cannot reproduce all the sources of uncertainties - environmental, operational, experimental - that affect responses recorded of the structure. The availability of data recorded on real structures provides precious material for the check of damage identification methods. In this paper the performance of the Interpolation Method for damage localization is investigated with reference to the real case study of a prestressed concrete road bridge, the S101 Bridge in Austria. The bridge, built in the early 1960, is a typical example of a European highway bridge. Responses to ambient vibration have been recorded both in the undamaged and in several different damage scenarios artificially inflicted to the bridge. Damage was introduced by lowering one of the bridge piers and by cutting prestressing tendons of one beam of the bridge deck.
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