Ground settlements induced by tunnel excavation cause the foundations of the neighboring building structures to deform. An expert system called NESASS( Neural network Expert System for Adjacent Structure Safety analysis) was developed to analyze the structural safety of such building structures. NESASS predicts the trend of ground settlements resulting from tunnel excavation and carries out a safety analysis for building structures on the basis of the predicted ground settlements. Using neural network technique. the NESASS learns the database consisting of the measured ground settlements collected from numerous actual fields and infers a settlement trend at the field of interest. The NESASS calculates the magnitudes of angular distortion, deflection ratio, and differential settlement of the structure. and in turn, determines the safety of the structure. In addition, the NESASS predicts the patterns of cracks to be formed in the structure, using Dulacska model for crack evaluation. In this study, the ground settlements measured from Seoul subway construction sites were collected and classified with respect to the major factors influencing ground settlement. Subsequently, a database of ground settlement due to tunnel excavation was built. A parametric study was performed to select the optimal neural network model for the database. A comparison of the ground settlement predicted by the NESASS with the measured ones indicates that the NESASS leads to reasonable predictions. The results of confidence evaluation for safety evaluation system of the NESASS are presented in this paper.