Abstract
This study entailed the development of an efficient service approach that integrates machine learning training results with other systems through experiments. The first model predicts the floor slab thickness by using bridge data from the engineering field. Girder bridge data were analyzed; correlation analysis was performed using the number of girders and spacing as characteristic values to predict the slab thickness. Storage methods for the training results and prediction models were systematized and linked to a retrieval system. As regards the communication method, the use of a Flask-based API was 160 times faster than direct execution. Further, this study focused on an efficient linkage method and implemented a design that stores not only existing model structures and training results but also hints regarding their characteristics so as to facilitate easy access by other users.
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