PSI - Issue 84

Nicola Perilli et al. / Procedia Structural Integrity 84 (2026) 813–820

814

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1. Introduction The Italian Guidelines for Risk Classification and Management, Safety Assessment, and Monitoring of Existing Bridges (LLG 2020) establish a structured multi-level risk assessment for existing bridges. A key component is the classification of structural, seismic, hydraulic, and landslide risk through the Class of Attention (CdA), based on periodic inspections. For landslides, the assessment of the CdA is based on three factors: susceptibility , vulnerability , and exposure (Table 1). As stated by LLG 2020 the assessment of the factor is based on periodic inspections performed by inspector, enabling the evaluation of bridge risk profiles and allowing bridge owners to maintain an updated overview of landslide CdA. Table 1. Primary and secondary parameters used for the determination of the Landslide Class of Attention ( LGG 20202).

Factor

Primary Parameters

Secondary Parameters

Model uncertainty (or Reliability of Assessment) Mitigation works

Susceptibility

Activity state, Velocity, Magnitude

Type/robustness of the bridge Type foundation

Vulnerability

Extent of interference

Road alternatives; Type of crossed entity; Strategic importance of the structure

Ground elevation (TGM) Span width

Exposure

However, discrepancies between the conceptual and operational procedures in LLG 2020 weaken the evaluation of primary and secondary parameters, affecting the reliability of the landslide CdA. This fragmentation reduces the quality of the collected data, slows decision-making, and hinders effective inspection prioritization. Despite advances in landslide susceptibility assessment and geospatial data management (Crosta & Frattini, 2013; Guzzetti, 2005; Pradhan & Lee., 2010), Despite advances in landslide susceptibility assessment and geospatial data management (Crosta & Frattini, 2013; Guzzetti, 2005; Pradhan & Lee, 2010), approaches and methodologies that integrate remote and field data collection for landslide mapping and monitoring remain limited. To address these challenges, the proposed procedures and tools integrate regional and local data within a dynamic WebGIS-based database management system, enabling structured storage, continuous updates, visualization, and accessibility of inspection data. The activities flow improves inspector performance and support bridge owner in effectively managing CdA results and prioritization of the periodic inspections. They also enable continuous updating of landslide inventories — including new or incipient landslides and landslide–bridge interactions – thereby supporting reliable assessments and the optimization of susceptibility models. 2. Conceptual and Operational Foundations for Landslide Class of Attention Assessment Before presenting the proposed structured three components, it is essential to clarify the significance of key terms used in LLG 2020 and their refined definitions according to Perilli et al. ( this volume ) . It is equally important to summarize the solutions proposed by Perilli et al. (2024a, 2024b, 2025b), which guide and support inspectors in systematically collecting back-office and field data (Level 0, Level 1) and in assessing the landslide CdA (Level 2). The Refinements and solutions help bridge owners to better plan and prioritize periodic inspections effectively. Refinements include: (i) clear conceptual and operational distinctions among landslide susceptibility , slope instability , and between potential unstable slopes , incipient landslides , and landslides ); (ii) redefinition of two of three secondary parameters used to assess landslide Factor of Susceptibility (see also Perilli et al. this volume ) . Table 2 presents operational definitions of Susceptibility , Slope Instability , and Susceptibility Class (LLG 2020) and their correspondence with Slope Instability Degree and Slope Instability Class , which also use potential unstable