PSI - Issue 84

Alessandro Scala et al. / Procedia Structural Integrity 84 (2026) 497–504

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3.3. Climate-resilient failure classification Table 1 summarizes how the presence of an extreme weather event influences both the prevailing failure mechanism and the associated damage severity for bridges affected by natural hazards. The analysis focuses on the two dominant natural causes identified in the database, namely floods and landslides, and reports, for each case, the distribution of the main subcategories together with the most frequently observed damage level, distinguishing between ordinary conditions and extreme events.

Table 1. Matrix showing the most frequent damage category and the percentages of cause subcategories as a function of the type of natural forcing and the presence or absence of an extreme event. Type of event w/o E.E. w/ E.E.

DL2 (50%) Scour (71%) Overtopping (29%)

DL3 (45%) Scour (82%) Overtopping (18%)

Flood

DL1 (56%) Slow landslides (86%) Rapid landslides (14%)

DL3 (80%) Slow landslides (50%) Rapid landslides (50%)

Landslide

For flood-related failures, foundation scour clearly emerges as the predominant collapse mechanism, accounting for approximately 71% of the cases in the absence of extreme events and remaining dominant under extreme conditions. Under ordinary scenarios, flood-induced failures are mainly associated with partial collapses, with Damage Level 2 (DL2) representing about 50% of the observed cases. The occurrence of extreme events does not substantially modify the relative contribution of the different hydraulic mechanisms, with only limited variations of approximately 10% in the distribution between scour and overtopping. However, a significant shift is observed in terms of damage severity: under extreme conditions, the prevailing damage level moves from DL2 to DL3, with total collapses accounting for approximately 45% of the cases. This trend confirms that increasing precipitation intensity primarily affects the severity of damage rather than the type of hydraulic failure mechanism, in agreement with the literature (Wang et al., 2017). The predominance of scour as the triggering cause, both in the presence and absence of extreme events, highlights its critical role in bridge vulnerability under hydraulic forcing. Different behaviour is observed for landslide-related failures. In the absence of extreme events, Damage Level 1 (DL1) is the most frequent outcome, accounting for approximately 56% of the cases and being predominantly associated with slow-moving landslides, which represent about 86% of the observed phenomena. Under these conditions, the progressive nature of slow landslides allows damage to develop gradually, often producing visible indicators such as cracking or pier displacements that enable timely intervention and risk mitigation. Conversely, when extreme weather events are involved, the damage scenario changes dramatically. As shown in Table 1, total collapses (DL3) become the dominant outcome, accounting for approximately 80% of the cases. At the same time, the relative contribution of slow and rapid landslides becomes balanced, with each mechanism representing about 50% of the failures. The increase in rapid landslide occurrences is consistent with the nature of these phenomena, which are typically triggered by intense and short-duration rainfall and tend to interact with the structure almost immediately (Canuti et al., 1985). Under such conditions, the time window for detecting precursor signals is drastically reduced or entirely suppressed, leading directly to severe or catastrophic structural damage. Overall, the results presented in Table 1 provide a synthetic framework linking natural forcing, failure mechanism, and damage severity under ordinary and extreme conditions. From a management perspective, this classification highlights how extreme events not only increase the likelihood of collapse but also alter the dominant failure pathways, limiting the effectiveness of inspection-based approaches and emphasizing the need for hazard-specific preparedness and early warning strategies in bridge risk management.