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Abstract

Bridge is the hub of the roads and plays a crucial role in transportation. In recent years, the age of most bridges in Taiwan has gotten old gradually; as a result, the bridges deteriorate and need to be fixed and maintained. Nevertheless, it is not easy to predict the timing of bridge deterioration, and such situation has become one of the important issues in introducing the concept of preventive maintenance. In fact, in order to control the condition of bridge deterioration, many countries have set up bridge management system and regulated methods and mechanism of inspection. The record of bridge maintenance and inspection history is therefore well preserved, but the number of researches utilizing value added analysis of bridge deterioration with database of history is limited. There are many factors that affect bridge deterioration. In addition to the materials and types of elements of the bridges, it is associated with external environments as well, including environmental factors as traffic flow or rainfall. The present paper adopts National Bridge Inventory (NBI) established by USA since 1992 to process data mining. Currently, the database is the most abundant and publicly accessible database of bridge history, not only collecting basic information of bridges in each state, but keeping a complete record of the inspection history every year. This research processes analysis by means of K-Means, Two –Steps, and C5.0, categorizing bridges into several different clusters and depicting the decision-making tree of clustering and rules of bridge deterioration. In the future, bridge maintenance personnel can gain a clear idea of the cluster of the bridges they are responsible for according to the results of the decision tree. They can also cope with possible deterioration in advance by introducing the concept of preventive maintenance management. Consequently, the disasters can be avoided, the safety of pedestrians can be ensured, and the bridge maintenance budget of public engineering can be economized.

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