Predicting pavement temperature by depth is crucial for road design, analysis, and maintenance. However, current methods predominantly utilize regression and/or open-form solutions focusing on highways. Additionally, most machine-learning models focus on asphalt layers and do not extend to deeper pavement layers. Therefore, this study provides deep-learning models using weather parameters to predict pavement temperature from surface to sublayers and estimate pavement freezing depth for developing massive apartment complexes. Temperature-by-depth data collected from thin pavements from three locations in South Korea were used. Comparative analyses of long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), and convolutional LSTM were performed. Results showed that CNN-LSTM model performed better with coefficients of determination ( R²) of 0.965, 0.987, and 0.981. Additionally, the CNN-LSTM predicted freezing depth with 0.3%–13.1% error margins outperforming the LSTM, Aldrich’s, and Korean Ministry of Transport approaches. The proposed approach shows that deep-learning models better estimate the freezing depth of pavements than existing approaches.

The successive growth of the aviation industry has progressively heightened the importance of airport pavement management systems. Existing research has primarily focused on the technological advancements of optimization models, with limited applicability in practice. In this study, we introduce condition cases optimization (CCO) to address these limitations while incorporating multi-facility and multi-year network optimization models. We developed condition index, serviceability level, integrated assessment indices and performance models for decision-making criteria. As a result, a practical decision-making strategy was proposed which can flexibly reflect budget constraints. Sensitivity analysis highlighted the impact of initial budget, maintenance methods, costs, and thresholds on decision outcomes. Using a case study, we validated the effectiveness and practicality of the CCO method as an efficient decision-making tool.

The rapid growth of the aviation industry has progressively heightened the importance of airport maintenance management. There is an increasing need for a comprehensive system that considers the diverse requirements of various stakeholders. Existing research has primarily focused on the technological advancements of optimization models, with limited applicability in practice. In this study, we introduce the Condition Cases Optimization (CCO) to address these limitations while incorporating multi-facility and multi-year network optimization models. We developed condition index, Serviceability Level, integrated assessment indices and performance models for decision-making criteria. We categorize conditions into three classes and propose a practical decision-making strategy that can flexibly adapt to budget constraints. Sensitivity analysis highlights the impact of initial budget, maintenance methods, costs, and thresholds on decision outcomes. Through a case study we validate the effectiveness and practicality of the CCO method as an efficient decision-making tool.

The historical management of underground utilities such as communication lines, water and sewage pipes, and power lines is essential for their effective use. However, due to the nature of the buried facilities, detecting and tracking them are challenging, although various solutions are difficult to apply in the field, especially optical cables, which are mainly used for communication, making it more difficult to apply existing solutions. There has been limited research on the practicalities of solutions, especially on multilayer structures such as road pavements. Based on a literature review, we selected ultra-high-frequency radio frequency identification (UHF RFID), which is least affected by performance degradation or interference due to batteries. We experimented to identify the signal attenuation based on the medium by controlling the materials and moisture used in the road pavement as variables. The signal reached a depth of 68 cm and this was possible under conditions of asphalt and subgrade soil. In a comparative experiment for each variable, we recognized tags of sand and coarse aggregate up to a depth of 70 cm. When the moisture was considered, the signal attenuation difference was 1.8 dBm for each level. Based on the experimental results of this study, it was confirmed that UHF RFID can be used as a marker sensor to be attached to utilities installed under the road pavement. Depending on the structure and material of the pavement, the signal is sufficiently transmitted up to 40–50 cm, which is the target installation depth of the communication line.