PSI - Issue 62

Laura Ierimonti et al. / Procedia Structural Integrity 62 (2024) 832–839 Ierimonti etal. / Structural Integrity Procedia 00 (2019) 000 – 000

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1. Introduction In the domain of bridge construction and management, the escalating integration of SHM systems marks a transformative data-driven era (Laflamme et al., 2023), a trend further supported by national codes, such as the Italian Guidelines on Risk Classification and Management of Bridges (LLGG, 2020). However, the growing prevalence of SHM introduces a new challenge: effectively managing the complexity of the generated data. Leveraging this wealth of information requires a high level of expertise, placing a substantial burden on bridge management systems. Among data-driven approaches, which rely solely on measured data to detect damage (Jiang et al., 2023; Bunce et al., 2023), model-based techniques are spreading out, which necessitates a deep understanding of the structural characteristics to reconstruct the mathematical model of the bridge. Recent advancements in SHM have embraced Bayesian model updating techniques, addressing various sources of uncertainty (Behmanesh and Moaveni, 2016; Ierimonti et al., 2021; Mao et al., 2023; Ierimonti et al., 2023;). Alongside SHM-based techniques, visual inspections of bridges are crucial for aging process tracking, safety assurance, risk mitigation and long-term maintenance planning. Hence, innovative, and user-friendly methodologies are essential, with a focus on optimizing the integration of diverse data sources to facilitate prompt and effective decision-making in maintenance practices. Data fusion, deployed at various levels, presents a promising avenue for enhancing the precision and dependability of SHM information. The recent surge in the adoption of Artificial Intelligence (AI)-based methodologies, though requiring substantial volumes of training data, has become notable. Given the restricted availability of labelled data for monitoring bridges, alternative techniques have gained prominence. Bayesian Networks (BNs) distinguish themselves as pivotal tools, thanks to their capacity to accommodate a wide range of uncertainties (Tubaldi et al., 2022). Notably, Dynamic Bayesian Networks (DBNs) are particularly compelling due to their integration of the time dimension (Xu et al., 2022), facilitating the efficient analysis of evolving system states. Despite being in their nascent stages within the field, DBNs have a huge potential to revolutionize the landscape of infrastructure monitoring. In the above depicted context, this paper introduces an innovative DBN-based unified framework for post-tensioned bridges, integrating SHM static data and results from visual inspections related to prestress losses. Encompassing key stages, including the selection of damage scenarios for SHM monitoring, numerical modeling, post-processing of SHM data, Bayesian model selection, and DBN-based data fusion, this framework aims to assess the risk of bridge failure. More in depth, DBNs enable handling incomplete or uncertain information, incorporating prior knowledge. In evaluating the performance of the proposed method, a single-span bridge serves as the testbed, simulating prestress- dependent damaging scenarios, environmental noise, and instrumental errors. To achieve this, a simple monitoring system based on inclinometers is employed, ensuring cost-effectiveness and ease of use. The inclinometers focus on measuring the inclination angle at the designated points. The data collected from these sensors can be utilized to estimate the deformed shape through analytical functions that establish a connection between the rotation and deflection. This testing scenario allows for a comprehensive assessment of the method's efficacy under varying conditions, providing insights into its robustness and reliability in real-world applications. The simplicity and affordability of the monitoring system further enhance the method's practicality and applicability across a range of bridge structures. The subsequent sections of this paper undertake an in-depth exploration of the framework, delving into its theoretical insights (Section 2), comprehensive framework description (Section 3), details of the case study (Section 4), main results (Section 5) and concluding insights. 2. Theoretical insights 2.1. Visual inspection-based damage assessment for post-tensioned bridges The visual inspection process conducted on existing bridges aims to evaluate the presence of surface defects, which may necessitate further examination. It is crucial to emphasize that the visual assessment of a bridge is fundamentally qualitative, providing a foundation for guiding repair and maintenance actions based on reports and photographic evidence of any structural anomalies. Aligned with LLGG 2020, the level of structural defectiveness (DL) is characterized by assigning discrete defect levels to each structural component. Each defect is defined by several