Abstract
Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data in order to obtain useful information about the condition of the bridge is not straight forward. This paper describes a case study of a railway bridge in India and explains how multi-dimensional visualization tools were used to extract relevant information from data. Parallel axis plots were used to visually examine the data. Trends and patterns in data were observed, which were used for more detailed investigation. The case study shows the complexity in data interpretation even in the case of simple bridge configurations.
Highlights
Structures such as bridges are increasingly being monitored for ensuring safety and for taking appropriate retrofit actions on time
actually on the bridge compared to the free vibrations induced when the train is approaching from the neighboring span
This demonstrates the complexities in the condition assessment of structures
Summary
Structures such as bridges are increasingly being monitored for ensuring safety and for taking appropriate retrofit actions on time. Structural responses have been used for other purposes as well, such as, construction monitoring and occupancy tracking (Pan et al, 2017; Poston et al, 2017; Harichandran et al, 2019). While sensor technology has progressed rapidly, methodologies for extracting useful information from data and incorporating them into the decision making process have not matured. Most work on data interpretation focus on removing unwanted effects from data in order to isolate useful information. Kromanis and Kripakaran (2017) discuss about separating the effects of temperature from structural response. Zhu et al (2019) use Moving Principal Component Analysis (MPCA) for accounting for temperature variations to detect structural anomalies. A few examples include Napolitano et al (2019), Glisic et al (2014), and Zonta et al (2014)
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