Preserving Coastal Infrastructure through the Design and Implementation of Image-Based Structural Health Monitoring (iSHM)
| 项目名称： | Preserving Coastal Infrastructure through the Design and Implementation of Image-Based Structural Health Monitoring (iSHM) |
| 摘要： | With infrastructure systems across the globe approaching the end of their service lives, there is an ever-pressing need for techniques to assess current condition and remaining life. As a case in point, bridges in the United States, with an average age approaching 45 years, represent one particular infrastructure system that is at risk. In this environment, deterioration has outpaced solutions for preservation and owners are faced with the challenges of assessing and managing this infrastructure without the resources and staffing necessary for proper management. This feature is particularly critical in coastal regions such as Hampton Roads, where high-profile infrastructure systems such as the Hampton Road Bridge Tunnel and Chesapeake Bay Bridge provide critical linkages along the Mid-Atlantic coastal corridor. The infrastructure in these coastal regions are particularly vulnerable to environmental change such as sea level rise extreme weather events, which not only has the potential to impact daily and event driven operation, but also impact the long-term performance as these structures are exposed to more extreme operational demands. Examples of these extreme operational demands include: larger and overloaded trucks, greater thermal cycles, more exposure to salting during snowstorm events, topside seawater exposure from storm surges, and underside exposure saltwater spray. Assessment represents one of the key components of the broader framework of structural health monitoring (SHM) and is essential to an overall mission of transportation sustainability, specifically infrastructure sustainability. Historically, much of this assessment has relied heavily on visual inspection as the standard method to characterize condition state, but research has shown that visual inspections yield results that are subjective and somewhat unreliable. While traditional visual assessment has a number of limitations when used in an subjective manner, vision as a quantitative tool is proving to be a powerful approach for assessment of condition and structural behavior. |
| 状态： | Completed |
| 资金： | 150,435 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Mid-Atlantic Transportation Sustainability Center |
| 执行机构： | University of Virginia, Charlottesville |
| 主要研究人员： | Harris, Devin K |
| 开始时间： | 20160801 |
| 预计完成日期： | 0 |
| 实际结束时间： | 20190220 |
相关文献
- Intelligent Structural Health Management of Civil Infrastructure
- Development of Fiber Optic Sensor Networks for Transportation Infrastructure Monitoring
- Multifunctional Composite Sensors for Sustainable Infrastructure Systems
- Automated and Contactless Identification of Asphalt Pavement Surface Friction Based on Computer Vision Image-based 3D Reconstruction and Machine Learning Techniques
- Non-contact Intelligent Inspection of Infrastructure
- Implementation of Structural Health Monitoring System
检索历史
应用推荐
