TY - GEN
T1 - A New Approach for Analyzing Safety and Performance Factors in Civil Infrastructures Using Correlation Networks and Population Analysis
AU - Chetti, Prasad
AU - Ali, Hesham H
AU - Ghersi, Dario
AU - Ghandi, Robin A
AU - Ricks, Brian
AU - Najjar, Lotfollah
N1 - Funding Information:
This work is partially funded by NSF awards 1762034 and 1636805. Drs. Deepak Khazanchi, Daniel Linzell, Chungwook Sim, and Saeed Eftekhar Azam provided valuable guidance during this research work and provided reviews for initial drafts.
Publisher Copyright:
© 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Public safety and economic growth are some of the key factors in motivating governments to keep their civil infrastructures, in particular bridges, safe and sound. However, the American Society for Civil Engineers gave a C+ grade for U.S. bridges in 2017. It has been observed that many parameters associated with bridges, such as geographical locations, designs, materials used, and traffic patterns, play key roles in determining the safety and deterioration rates of bridges. However, there is still a lack of studies that analyze the exact impact of all relevant parameters. The motivation of this study is to propose a new data-driven model that employs the concept of population analysis in assessing the impact of each potential parameter and extracting critical information associated with civil infrastructures and their deterioration patterns. We use a correlation network model to analyze and visualize the big data associated with more than 600,000 bridges in the national bridge inventory database. Graph theoretic analysis is applied to the correlation networks to find elements or clusters of interest. A sub-set of 268 bridges across the US of the same age are considered for this case study and the Markov clustering algorithm is used to obtain the clusters from the correlation network. Enrichment analysis is applied to the clusters to identify the significantly enriched input parameters. Preliminary results reveal several facts, including that prestressed concrete bridges in the Southeast region perform better than steel bridges in the Midwestern region. The obtained results are supported by previous research and further validated by the exploratory factor analysis when dividing the clusters into two groups. The proposed network model provides a new data-driven methodology for evaluating the safety and performance of structures. It provides domain experts with valuable information on how to efficiently allocate time and funds for inspecting existing bridges and how to identify key bridge parameters suitable for designing and constructing new bridges in various geographical areas.
AB - Public safety and economic growth are some of the key factors in motivating governments to keep their civil infrastructures, in particular bridges, safe and sound. However, the American Society for Civil Engineers gave a C+ grade for U.S. bridges in 2017. It has been observed that many parameters associated with bridges, such as geographical locations, designs, materials used, and traffic patterns, play key roles in determining the safety and deterioration rates of bridges. However, there is still a lack of studies that analyze the exact impact of all relevant parameters. The motivation of this study is to propose a new data-driven model that employs the concept of population analysis in assessing the impact of each potential parameter and extracting critical information associated with civil infrastructures and their deterioration patterns. We use a correlation network model to analyze and visualize the big data associated with more than 600,000 bridges in the national bridge inventory database. Graph theoretic analysis is applied to the correlation networks to find elements or clusters of interest. A sub-set of 268 bridges across the US of the same age are considered for this case study and the Markov clustering algorithm is used to obtain the clusters from the correlation network. Enrichment analysis is applied to the clusters to identify the significantly enriched input parameters. Preliminary results reveal several facts, including that prestressed concrete bridges in the Southeast region perform better than steel bridges in the Midwestern region. The obtained results are supported by previous research and further validated by the exploratory factor analysis when dividing the clusters into two groups. The proposed network model provides a new data-driven methodology for evaluating the safety and performance of structures. It provides domain experts with valuable information on how to efficiently allocate time and funds for inspecting existing bridges and how to identify key bridge parameters suitable for designing and constructing new bridges in various geographical areas.
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M3 - Conference contribution
AN - SCOPUS:85132970485
T3 - Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021
SP - 593
EP - 600
BT - Structural Health Monitoring 2021
A2 - Farhangdoust, Saman
A2 - Guemes, Alfredo
A2 - Chang, Fu-Kuo
PB - DEStech Publications Inc.
T2 - 13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021
Y2 - 15 March 2022 through 17 March 2022
ER -