| Title |
Condition Estimation of Facility Elements Using XGBoost |
| Authors |
Chang, Taeyeon ; Yoon, Sihoo ; Chi, Seokho ; Im, Seokbeen |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2023.24.1.031 |
| Keywords |
Facility Management; FMS; Element Condition Estimation; XGBoost |
| Abstract |
To reduce facility management costs and safety concerns due to aging of facilities, it is important to estimate the future facilities’ condition based on facility management data and utilize predictive information for management decision making. To this end, this study proposed a methodology to estimate facility elements’ condition using XGBoost. To validate the proposed methodology, this study constructed sample data for road bridges and developed a model to estimate condition grades of major elements expected in the next inspection. As a result, the developed model showed satisfactory performance in estimating the condition grades of deck, girder, and abutment/pier (average F1 score 0.869). In addition, a testbed was established that provides data management function and element condition estimation function to demonstrate the practical applicability of the proposed methodology. It was confirmed that the facility management data and predictive information in this study could help managers in making facility management decisions. |