PSI - Issue 64

Filippo Andreose et al. / Procedia Structural Integrity 64 (2024) 40–47

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© 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers Keywords: Bridge; SHM; Vibration Modes; OMA. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SMAR 2024 Organizers 1. Introduction Some recent tragic collapses have revealed the fundamental challenge posed by ageing civil infrastructure and the need to prioritize its management in the political agenda. This has promoted a vast volume of research on SHM in the last two decades, as well as the publication of a multitude of technical standards of SHM worldwide (ISIS Canada (2001); UNI/TR11634(2016); Ministry of Infrastructure Decree no. 578, 2020 (2020); Moreu et al. (2023)). SHM can be defined as the process of implementing a continuous damage identification strategy for an engineering infrastructure. Damage can be conceived in this context as any disruptive event affecting the system’s performance such as, for instance, alterations of the material and/or geometrical properties, or changes to the boundary conditions/connectivity (Fuentes et al. (2020)). In this framework, ambient vibration-based SHM has received most attention owing to its minimal intrusiveness, non-destructive character, and global damage identification capabilities (Fritzen (2005)). These techniques exploit the ambient vibrations of mechanical systems under normal operating conditions to extract their modal features (i.e. resonant frequencies, damping ratios and mode shapes) through Operational Modal Analysis (OMA) (Bin et al. (2020)). Then the appearance of damage affecting the stiffness and/or the energy dissipation properties of the system can be inferred from permanent variations in its modal characteristics (Saisi et al. (2015), Gentile et al. (2015), Lon Wah Soo et al. (2018), Ubertini et al. (2018)). In this light, considerable research has been devoted in recent years to the development of automated OMA procedures to enable their use in continuous SHM schemes. Numerous research works have also reported about the frequent existence of important effects of environmental and operational conditions (EOCs) (e.g. temperature, humidity, wind, traffic intensity) upon the structural response of civil engineering assets. A remarkable example was provided by Peeters and De Roeck (2001) who found temperature driven variations of up to 18% of the fundamental frequency of the well-known case study of the Z24-Bridge. Correlations between EOCs and the structural response may be disparate depending on manifold factors such as the construction materials, structural typology, connectivity, solar radiation, thermal capacitance, to mention a few. The effects of EOCs translate into daily and seasonal trends in the monitoring data, which often mask the appearance of anomalies induced by damage and thus need to be filtered out to attain effective damage identification. This process, also referred to as data normalization, is usually conducted through statistical pattern recognition (SPR) and machine learning techniques (Farrar et al. (2012)). In order to allow an extensive application of the abovementioned concepts to real case applications, a new software platform has been developed by the research groups of the University of Perugia, Politecnico of Milan and University of Padova within the research activities of the FABRE Consortium which develops research in the field of assessment and monitoring of bridges (https://www.consorziofabre.it/). In this paper, the new software code for the autonomous management of integrated SHM systems, named P3P (acronym standing for the SHM of “ P onti” (i.e. bridges in Italian) originally developed by “3P team” of researchers belonging to P erugia, P olitecnico of Milan and P adova universities), is presented. P3P has been developed within a research project funded by Anas S.p.A, the largest Italian company dedicated to the management of the Italian roadway system, and it is specifically designed for SHM of highway bridges, representing a further evolution of MOVA/MOSS suite [Garcia-Macias et al. (2020)]. In the second part of the work, some applications are briefly described to show the potentialities of the tool in the context of SHM of different bridge configurations . 2. Software architecture Figure 1 illustrates the typical workflow of P3P for an SHM system permanently installed on a bridge. In general, the monitoring system consists of an integrated sensor network deployed on the structure comprising dynamic, environmental and static sensors. An in-place data acquisition system (DAQ) permanently collects data from the