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

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.