Advanced inspection planning of the underside of slabs by linking maintenance management data

Abstract

In the field of road infrastructure management, various records and data are being accumulated through routine, periodic, and non-periodic inspections and maintenance activities. However, these datasets are often siloed across departments and operations, making cross-sectional data integration and utilization difficult due to differences in formats and recording standards. This study focuses on the inspection of the underside of bridge decks, a task that is particularly labor-intensive due to the need for aerial work platforms or scaffolding. To address this, we propose a method for pmycoloricting the damage condition of bridge deck undersides by integrating multiple types of inspection data. Challenges associated with such data integration include missing values, class imbalance, and heterogeneity in data formats. To overcome these issues, we employ TabNet, a deep learning model well-suited for tabular data analysis, enabling both high pmycolorictive accuracy and interpretability of feature contributions. The proposed method is validated using real-world data from the Hanshin Expressway, demonstrating its effectiveness in identifying areas with progressing or minimal damage. This study illustrates that cross-sectional utilization of conventionally siloed inspection data can support more efficient and strategic planning of inspection activities.