In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing ...In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions.The paper proposes a multi-level strategy,designed and implemented on the basis of periodic structural monitoring oriented to a cost-and time-efficient tunnel control plan.Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations.In a supervised learning framework,Ground Penetrating Radar(GPR)profiles and the revealed structural phenomena have been used as input and output to train and test such networks.Image-based analysis and integrative investigations involving video-endoscopy,core drilling,jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database.The degree of detail and accuracy achieved in identifying a structural condition is high.As a result,this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing,and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.展开更多
On 18 January 2017 a catastrophic avalanche destroyed the Rigopiano Gran Sasso Resort&Wellness(Rigopiano Hotel)in the Gran Sasso National Park in Italy,with 40 people trapped and a death toll of 29.This article de...On 18 January 2017 a catastrophic avalanche destroyed the Rigopiano Gran Sasso Resort&Wellness(Rigopiano Hotel)in the Gran Sasso National Park in Italy,with 40 people trapped and a death toll of 29.This article describes the location of the disaster and the general meteorological scenario,with field investigations to provide insight on the avalanche dynamics and its interaction with the hotel buildings.The data gathered in situ suggest that the avalanche was a fluidized dry snow avalanche,which entrained a sligthtly warmer snow cover along the path and reached extremely long runout distances with braking effect from mountain forests.The avalanche that reached the Rigopiano area was a‘‘wood-snow’’avalanche—a mixture of snow and uprooted and crushed trees,rocks,and other debris.There were no direct eyewitnesses at the event,and a quick post-event survey used a numerical model to analyze the dynamics of the event to estimate the pressure,velocity,and direction of the natural flow and the causes for the destruction of the hotel.Considering the magnitude and the damage caused by the event,the avalanche was at a high to very high intensity scale.展开更多
文摘In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions.The paper proposes a multi-level strategy,designed and implemented on the basis of periodic structural monitoring oriented to a cost-and time-efficient tunnel control plan.Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations.In a supervised learning framework,Ground Penetrating Radar(GPR)profiles and the revealed structural phenomena have been used as input and output to train and test such networks.Image-based analysis and integrative investigations involving video-endoscopy,core drilling,jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database.The degree of detail and accuracy achieved in identifying a structural condition is high.As a result,this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing,and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.
文摘On 18 January 2017 a catastrophic avalanche destroyed the Rigopiano Gran Sasso Resort&Wellness(Rigopiano Hotel)in the Gran Sasso National Park in Italy,with 40 people trapped and a death toll of 29.This article describes the location of the disaster and the general meteorological scenario,with field investigations to provide insight on the avalanche dynamics and its interaction with the hotel buildings.The data gathered in situ suggest that the avalanche was a fluidized dry snow avalanche,which entrained a sligthtly warmer snow cover along the path and reached extremely long runout distances with braking effect from mountain forests.The avalanche that reached the Rigopiano area was a‘‘wood-snow’’avalanche—a mixture of snow and uprooted and crushed trees,rocks,and other debris.There were no direct eyewitnesses at the event,and a quick post-event survey used a numerical model to analyze the dynamics of the event to estimate the pressure,velocity,and direction of the natural flow and the causes for the destruction of the hotel.Considering the magnitude and the damage caused by the event,the avalanche was at a high to very high intensity scale.
