In this study, the acquisition of rule-type knowledge from field inspection data on highway
bridges is enhanced by introducing an improvement to a traditional data mining technique, i.e. applying the
rough set theory to the traditional decision table reduction method. 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. Instead of automatically removing all apparently contradictory data cases, the
new method determines whether the data really is contradictory and therefore must be removed or not. The
new method is tested with real data on bridge members including girders and filled joints in bridges owned
and managed by a highway corporation in Japan. There are, however, numerous inconsistent data in field data.
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.