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.

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The Taiwan Bridge Management System (TBMS) has been online since 2000 and the total amount of inventory is 33,275, including all kinds of bridges and culverts. Currently, the number of fields in all tables in the databases of TBMS is around 6,500 with more than 3.4 million data records in its databases. There are more than 11,200 bridges that are over 20 years old with another 9,300 bridge having unknown built years in the TBMS. The bridges in Taiwan have stepped into the stage where maintenance is crucial and frequently required. Therefore, this research aims at analyzing the database in the TBMS using Exploratory Factor Analysis for determining maintenance strategies for these bridges. This paper describes results of the first year's research efforts. Relevant literature in bridge maintenance, prioritization, and life-cycle bridge management were firstly reviewed. Concepts, theories, and available software for analyzing "Big Data" were also introduced. 