Advisor Information
Dr. Hesham H.Ali
Location
Criss Library
Presentation Type
Poster
Start Date
1-3-2019 10:45 AM
End Date
1-3-2019 12:00 PM
Abstract
Many recent studies have shown that a large percentage of bridges in many parts of the world have low safety rating. National Bridge Inventory (NBI) database contains the information of more than 600,000 bridges, where each bridge has 116 parameters. Current safety inspections require bridge inspectors to manually inspect each bridge every few years. Manpower and budget constraints limit such approach from inspecting the bridges more frequently. Clearly, more efficient approaches need to be developed to improve the process of bridge inspection and increase the overall safety of bridges and civil infrastructures. In this study, we propose a Correlation Network Model (CNM) to analyze and visualize the big-data associated with more than 600,000 bridges of NBI database. We use Correlation Networks based on various safety parameters (deck rating in this case), then apply community clustering algorithm such as GLay to analyze a population of 8,712 Nebraska non-culvert bridges. Our results show that out of top5 clusters, two clusters have highly negative correlations with average deck ratings and one cluster have highly negative correlation with the average daily traffic. So these clusters need more attention than other cluster as these clusters are sensitive to the age and average daily traffic.
A correlation network model for managing safety and performance issues in bridges and civil infrastructures
Criss Library
Many recent studies have shown that a large percentage of bridges in many parts of the world have low safety rating. National Bridge Inventory (NBI) database contains the information of more than 600,000 bridges, where each bridge has 116 parameters. Current safety inspections require bridge inspectors to manually inspect each bridge every few years. Manpower and budget constraints limit such approach from inspecting the bridges more frequently. Clearly, more efficient approaches need to be developed to improve the process of bridge inspection and increase the overall safety of bridges and civil infrastructures. In this study, we propose a Correlation Network Model (CNM) to analyze and visualize the big-data associated with more than 600,000 bridges of NBI database. We use Correlation Networks based on various safety parameters (deck rating in this case), then apply community clustering algorithm such as GLay to analyze a population of 8,712 Nebraska non-culvert bridges. Our results show that out of top5 clusters, two clusters have highly negative correlations with average deck ratings and one cluster have highly negative correlation with the average daily traffic. So these clusters need more attention than other cluster as these clusters are sensitive to the age and average daily traffic.