The Sichuan Tibet railway is a key project under planning and construction in China. This complex project passes through the eastern part of the Qinghai-Tibet Plateau where the topography and geological structure are extremely complex. Active faults are well developed along the Sichuan Tibet railway, and the potential risk of strong earthquakes is high. Under the action of internal and external dynamic coupling, the geological disasters along the railway are extremely developed. The Large landslide is one of the common geological disasters along the Sichuan-Tibet Railway, which has the characteristics of wide distribution, strong concealment, and sudden occurrence, and brings great risks to the safe construction of the railway. Therefore, it is of great theoretical and practical value to explore the early identification, formation mechanism, and mitigation technology of large landslides.
The formation and evolution mechanisms of large landslides are very complicated and influenced by many aspects. Field investigations, laboratory tests, theoretical models, and numerical methods can help better understand the formation mechanisms and dynamics, and provide more reasonable evaluation and forecasting results. Recently, studies on the recognition and mechanism of large landslides have improved the level of early warning technology for large landslides. However, there are still many challenges in the quantitative identification of large landslides, real-time stability evaluation, and monitoring indicators applicable to the field. This Research Topic aims to explore the quantitative identification, formation mechanism, evolution process, and risk assessment methods of large landslides, to better understand the distribution and long-term evolution process of large landslides and improve the reliability of regional landslide risk assessment. We welcome submissions of original research and review articles that will help to advance the current frontiers in the physics of large landslides, whether from theoretical modeling, utilizing laboratory or numerical experiments, based on field and remote sensing observations, or from the point of view of social and computational sciences, including the application of artificial intelligence to model landslides susceptibility, vulnerability, and risk. Potential topics include but are not limited to the following:
• Downscaled laboratory or numerical experiments on any type of physical system relevant to large landslides.
• Quantitative dynamic damage identification of large landslides.
• Formation mechanism of large landslides subjected to environmental factors (earthquake and rainfall, etc.) and engineering load factors (excavation, blasting, reservoir, etc.).
• Application research of new monitoring technology and construction of early warning systems for large landslides.
• Risk assessment method for large landslides on a regional scale.
• Large landslides and their contribution to long-term erosion.
• Multi-sensor and multi-platform remote sensing characterization of large landslides.
The Sichuan Tibet railway is a key project under planning and construction in China. This complex project passes through the eastern part of the Qinghai-Tibet Plateau where the topography and geological structure are extremely complex. Active faults are well developed along the Sichuan Tibet railway, and the potential risk of strong earthquakes is high. Under the action of internal and external dynamic coupling, the geological disasters along the railway are extremely developed. The Large landslide is one of the common geological disasters along the Sichuan-Tibet Railway, which has the characteristics of wide distribution, strong concealment, and sudden occurrence, and brings great risks to the safe construction of the railway. Therefore, it is of great theoretical and practical value to explore the early identification, formation mechanism, and mitigation technology of large landslides.
The formation and evolution mechanisms of large landslides are very complicated and influenced by many aspects. Field investigations, laboratory tests, theoretical models, and numerical methods can help better understand the formation mechanisms and dynamics, and provide more reasonable evaluation and forecasting results. Recently, studies on the recognition and mechanism of large landslides have improved the level of early warning technology for large landslides. However, there are still many challenges in the quantitative identification of large landslides, real-time stability evaluation, and monitoring indicators applicable to the field. This Research Topic aims to explore the quantitative identification, formation mechanism, evolution process, and risk assessment methods of large landslides, to better understand the distribution and long-term evolution process of large landslides and improve the reliability of regional landslide risk assessment. We welcome submissions of original research and review articles that will help to advance the current frontiers in the physics of large landslides, whether from theoretical modeling, utilizing laboratory or numerical experiments, based on field and remote sensing observations, or from the point of view of social and computational sciences, including the application of artificial intelligence to model landslides susceptibility, vulnerability, and risk. Potential topics include but are not limited to the following:
• Downscaled laboratory or numerical experiments on any type of physical system relevant to large landslides.
• Quantitative dynamic damage identification of large landslides.
• Formation mechanism of large landslides subjected to environmental factors (earthquake and rainfall, etc.) and engineering load factors (excavation, blasting, reservoir, etc.).
• Application research of new monitoring technology and construction of early warning systems for large landslides.
• Risk assessment method for large landslides on a regional scale.
• Large landslides and their contribution to long-term erosion.
• Multi-sensor and multi-platform remote sensing characterization of large landslides.
