Rule-type Knowledge Discovery from Field Inspection Data of Bridges including Contradictory Data using Rough-sets based Data Mining Technique

In this study, the acquisition of rule-type knowledge from field inspection data on highway bridges isenhanced by introducing an improvement to a traditional data mining technique. The new rough set theory approach helps in cases of exceptional and contradictory data, which in the traditional decision table reduction method are simply removed from analyses. There are numerous inconsistent data in real data owned and managed by a highway corporation in Japan. A new method is therefore proposed to solve the problem of data loss. The new method reveals some generally unrecognized decision rules in addition to generally accepted knowledge. Finally, a computer programs is developed to perform calculation routines, and some field inspection data on highway bridges is used to show the applicability of the proposed method.