Building information modeling (BIM) has been widely applied in conjunction with the industry foundation class (IFC) for buildings and infrastructure such as railways. However, a limitation of the BIM technology presents limitations that make designing the three-dimensional (3D) alignment-based information models difficult. Thus, the time and effort required to create a railway track model are increased, while the reliability of the model is reduced. In this study, we propose a methodology for developing an alignment-based independent railway track model and extended IFC models containing railway alignment information. The developed algorithm using BIM software tools allows for a discontinuous structure to be designed. The 3D alignment information connects different BIM software tools, and the classification system and IFC schema for expressing railway tracks are extended. Moreover, the classification system is fundamental for assigning IFC entities to railway components. Spatial and hierarchical entities were created through a developed user interface. The proposed methodology was implemented in an actual railway track test. The possibility of managing IFC-based railway track information, including its 3D alignment information, was confirmed. The proposed methodology can reduce the modeling time and can be extended to other alignment-based structures, such as roads.

Reinforced concrete slab (RCS) bridges deteriorate because of exposure to environmental factors over time, resulting in reduced durability. Particularly, the carbonation of RCS bridges corrodes the rebars and reduces the strength. However, carbonation models derived from short-term experiments exhibit low reliability with respect to existing bridges. Therefore, a long short-term memory (LSTM)-based methodology was developed in this study for generating carbonation models using existing bridge inspection reports. The proposed methodology trains the LSTM model by combining data extracted from reports and local environmental data. The learning process uses padding and masking methods to consider the history of environmental data. A case study was performed to validate the proposed method in three different regions of Korea. The results verified that the coefficient of determination of the proposed method was higher than those of the existing carbonation models and other regression analyses. Therefore, the developed methodology can be used for predicting regional carbonation models using the data from existing bridges.

To cope with the premature deterioration of reinforced concrete structures caused by chloride-induced corrosion, the corrosion rate is required to be estimated and the transport parameters calculated. The electrochemical impedance spectroscopy (EIS) technique can evaluate steel corrosion behaviors at different frequencies. However, its capacity to interpret the impedance response of the system (steel embedded in concrete) is still challenged. Although concrete is a good barrier used to secure structural performance, one of the main obstacles associated with electrical measurements relates to the cases in which concrete contains unexpected or invisible variables, such as changes in pore structure induced by the continuous hydration process. In addition, the fact that steel embedded in concrete is controlled during experiments is technically challenging. Due to these limitations, various circuits have been proposed to explain the corrosion behavior of steel in concrete. EIS measurements are very sensitive to small perturbations. During the analysis process of EIS data, it is possible to introduce unexpected errors attributed to variables; thus, the estimated corrosion values based on the analysis rate may be misleading. To overcome the limitations, it is necessary to confirm the impedance responses first in controlled variable cases. In this study, EIS measurements were conducted for active/passive states of steel in concrete in various conditions to minimize variable errors which are likely induced by operators.

The Health Index (HI) serves as an essential tool for assessing the structural and functional condition of bridges, calculated based on the condition of structural components and the serviceability of the bridge. Its primary purpose is to identify the most deteriorated structures in an asset inventory and prioritize those in most urgent need of repair. However, a frequently cited issue is the lack of accurate and objective data, with the determination of the HI often being heavily reliant on expert opinions and engineering judgment. Furthermore, the HI systems used in most countries are dependent on the current state of bridge components, making it challenging to use as a proactive indicator for factors such as the rate of bridge aging. To address this issue, this study introduces a novel HI as a quantitative evaluation metric for reinforced concrete slab bridges and details the process of deriving the HI based on deterioration models. The deterioration models are derived by preprocessing the deterioration data of reinforced concrete (RC) slab bridges, wherein the relationship between time and deterioration is directly employed for training a long short-term memory model. The HI was validated through a case study involving six RC slab bridges, wherein accuracies of >93% were achieved, confirming that the proposed quantitative evaluation methodology can significantly contribute to maintenance decisions for bridges.