{"title":"Landslide-bridge interaction: Insights from an extensive database of Italian case studies","authors":"Diana Salciarini , Erica Cernuto , Giulia Capati , Francesca Dezi , Lorenzo Brezzi , Fabiola Gibin , Fabio Gabrieli , Stefano Stacul , Angelo Doglioni , Arianna Lupattelli , Nunziante Squeglia , Vincenzo Simeone , Paolo Simonini","doi":"10.1016/j.ijdrr.2024.104983","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the wealth of documented case studies, systematic approaches to correlate landslide characteristics with the damage they cause to bridges are rare. The correlation is challenging due to the complexity of landslides, which can vary in movement types, volume, velocities, materials, and orientations. Additionally, the lack of universally applicable models to forecast bridge responses in case of landslide interaction adds complexity. Recognizing the urgency of addressing this challenge, various countries, including Italy, have introduced guidelines and strategies to manage infrastructure risks and enhance safety. Efforts are underway to develop practical tools for authorities and infrastructure managers, encompassing factors influencing bridge response, especially under the action of natural hazards. This article presents a database of landslide-bridge interactions in Italy, developed under the FABRE Consortium. The database was compiled by analysing 382 bridges across 12 Italian regions. The article explores correlations between landslide characteristics and risk classification for bridges, defined as “Landslide Class of Attention” (L-CoA). The analysis shows that landslide volume is directly correlated with L-CoA severity, with larger volumes leading to higher classifications. Very slow-moving landslides are prevalent in high-risk L-CoA categories, suggesting they are associated with significant volumes and severe consequences. Complete interference between landslides and infrastructure poses the highest risk, while partial interference also contributes significantly. Combined landslides tend to result in more severe L-CoA classifications. The findings underscore the importance of better understanding the interactions between landslides and bridges, to develop predictive models and mitigate the risks posed by landslides to infrastructure in Italy and beyond.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"114 ","pages":"Article 104983"},"PeriodicalIF":4.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212420924007453","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the wealth of documented case studies, systematic approaches to correlate landslide characteristics with the damage they cause to bridges are rare. The correlation is challenging due to the complexity of landslides, which can vary in movement types, volume, velocities, materials, and orientations. Additionally, the lack of universally applicable models to forecast bridge responses in case of landslide interaction adds complexity. Recognizing the urgency of addressing this challenge, various countries, including Italy, have introduced guidelines and strategies to manage infrastructure risks and enhance safety. Efforts are underway to develop practical tools for authorities and infrastructure managers, encompassing factors influencing bridge response, especially under the action of natural hazards. This article presents a database of landslide-bridge interactions in Italy, developed under the FABRE Consortium. The database was compiled by analysing 382 bridges across 12 Italian regions. The article explores correlations between landslide characteristics and risk classification for bridges, defined as “Landslide Class of Attention” (L-CoA). The analysis shows that landslide volume is directly correlated with L-CoA severity, with larger volumes leading to higher classifications. Very slow-moving landslides are prevalent in high-risk L-CoA categories, suggesting they are associated with significant volumes and severe consequences. Complete interference between landslides and infrastructure poses the highest risk, while partial interference also contributes significantly. Combined landslides tend to result in more severe L-CoA classifications. The findings underscore the importance of better understanding the interactions between landslides and bridges, to develop predictive models and mitigate the risks posed by landslides to infrastructure in Italy and beyond.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.