A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan

Yu-Han Chuang (), Nie-Jia Yau and John Mark M. Tabor
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Yu-Han Chuang: Department of Civil Engineering, National Central University, No. 300, Chung-da Rd., Taoyuan 32011, Taiwan
Nie-Jia Yau: Department of Civil Engineering, National Central University, No. 300, Chung-da Rd., Taoyuan 32011, Taiwan
John Mark M. Tabor: Department of Civil Engineering, National Central University, No. 300, Chung-da Rd., Taoyuan 32011, Taiwan

Sustainability, 2023, vol. 15, issue 2, 1-10

Abstract: Due to the dwindling maintenance budget and lack of qualified bridge inspectors, bridge-management agencies in Taiwan need to develop cost-effective maintenance and inspection strategies to preserve the safety and functionality of their aging, natural disaster-prone bridges. To inform the development of such a strategy, this study examined the big data stored in the Taiwan Bridge Management System (TBMS) using the knowledge discovery in databases (KDD) process. Cluster and association algorithms were applied to the inventory and five-year inspection data of 2849 bridges to determine the bridge structural configurations and components that are prone to deterioration. Bridge maintenance agencies can use the results presented to reevaluate their current maintenance and inspection strategies and concentrate their limited resources on bridges and components most prone to deterioration.

Keywords: bridge management system; bridge maintenance; bridge inspection; big data; knowledge discovery in databases; data mining; cluster analysis; association analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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