The risk of collapse at medium and long term of architectural heritage structures made by masonry, timber, steel and reinforced concrete has been rapidly growing worldwide because of global warming. To the increasing alarm caused by climate changes, the seismic matter always represents an additional constant concern for those structures located in earthquake prone areas.The need of preservation and protection holds in both cases. Failures can be almost immediate, as in the case of earthquakes, strong winds and floods of exceptional intensity, or can trigger in a relatively long timeframe throughout a dangerous slow progression, as it occurs for instance in extreme droughts, or for temperature fluctuations. Strong winds also represent an obvious cause of failure and extreme floods can generate hydrodynamic forces in historical structures, creating damages or lead them to collapse.Preservation implies the need of investigating with a certain level of predictivity and reliability the expected future behavior, giving an insight into the most suitable interventions to implement with the aim of protecting it -in agreement with strict constraints dictated by restoration- in a scenario where loads applied are expected to increase. In this framework, long term health monitoring and the implementation of new combined experimental and numerical techniques based on IoT, machine learning, AI and Digital Twins is of particular interest for the great potential in the application on structures where mini-invasiveness and quasi-real time prediction of the collapse triggering appear paramount.The special issue is targeted to both methodological approaches and paradigmatic case studies devoted to the failure analysis of built heritage structures subjected to any extreme event. Methodologies include experimental and numerical approaches, belongins to structural engineering, mechanics of materials, metrology, chemistry, informatics, telecommunications, preservation, etc.
Failure analysis of existing structures and infrastructures under extreme events and long-term actions
Milani G.;
2024-01-01
Abstract
The risk of collapse at medium and long term of architectural heritage structures made by masonry, timber, steel and reinforced concrete has been rapidly growing worldwide because of global warming. To the increasing alarm caused by climate changes, the seismic matter always represents an additional constant concern for those structures located in earthquake prone areas.The need of preservation and protection holds in both cases. Failures can be almost immediate, as in the case of earthquakes, strong winds and floods of exceptional intensity, or can trigger in a relatively long timeframe throughout a dangerous slow progression, as it occurs for instance in extreme droughts, or for temperature fluctuations. Strong winds also represent an obvious cause of failure and extreme floods can generate hydrodynamic forces in historical structures, creating damages or lead them to collapse.Preservation implies the need of investigating with a certain level of predictivity and reliability the expected future behavior, giving an insight into the most suitable interventions to implement with the aim of protecting it -in agreement with strict constraints dictated by restoration- in a scenario where loads applied are expected to increase. In this framework, long term health monitoring and the implementation of new combined experimental and numerical techniques based on IoT, machine learning, AI and Digital Twins is of particular interest for the great potential in the application on structures where mini-invasiveness and quasi-real time prediction of the collapse triggering appear paramount.The special issue is targeted to both methodological approaches and paradigmatic case studies devoted to the failure analysis of built heritage structures subjected to any extreme event. Methodologies include experimental and numerical approaches, belongins to structural engineering, mechanics of materials, metrology, chemistry, informatics, telecommunications, preservation, etc.| File | Dimensione | Formato | |
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