Knowledge integrated visual analysis system for in-depth management of bridge safety and maintenance

Xiaoyu Wang; Wenwen Dou; Rashna Vatcha; Wanqiu Liu; Shen-En Chen; Seok-Won Lee; Remco Chang; William Ribarsky

doi:10.1117/12.818405

30 April 2009 Knowledge integrated visual analysis system for in-depth management of bridge safety and maintenance

Xiaoyu Wang, Wenwen Dou, Rashna Vatcha, Wanqiu Liu, Shen-En Chen, Seok-Won Lee, Remco Chang, William Ribarsky

Proceedings Volume 7346, Visual Analytics for Homeland Defense and Security; 734606 (2009) https://doi.org/10.1117/12.818405
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Infrastructure safety affects millions of U.S citizens in many ways. Among all the infrastructures, the bridge plays a significant role in providing substantial economy and public safety. Nearly 600,000 bridges across the U.S are mandated to be inspected every twenty-four months. Although these inspections could generate great amount of rich data for bridge engineers to make critical maintenance decisions, processing these data has become challenging due to the low efficiency from those traditional bridge management systems. In collaboration with North Carolina Department of Transportation (NCDOT) and other regional DOT collaborators, we present our knowledge integrated visual analytics bridge management system. Our system aims to provide bridge engineers a highly interactive data exploration environment as well as knowledge pools for corresponding bridge information. By integrating the knowledge structure with visualization system, our system could provide comprehensive understandings of the bridge assets and enables bridge engineers to investigate potential bridge safety issues and make maintenance decisions.

Citation Download Citation

Xiaoyu Wang, Wenwen Dou, Rashna Vatcha, Wanqiu Liu, Shen-En Chen, Seok-Won Lee, Remco Chang, and William Ribarsky "Knowledge integrated visual analysis system for in-depth management of bridge safety and maintenance", Proc. SPIE 7346, Visual Analytics for Homeland Defense and Security, 734606 (30 April 2009); https://doi.org/10.1117/12.818405

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