|
Title |
Evaluation of Data-based Expansion Joint-gap for Digital Maintenance
|
|
Authors |
박종호(Jongho Park) ; 신유성(Yooseong Shin) |
|
DOI |
https://doi.org/10.11112/jksmi.2024.28.2.1 |
|
Keywords |
신축이음 유간; 설명 가능한 AI; 데이터 기반 유지관리; 인공지능 Expansion joint-gap; Explainable AI; Data-based maintenance; Artificial intelligence |
|
Abstract |
The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.
|