Mobile QR Code QR CODE
Export citation EndNote

References

1 
Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C. (2004) A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. ACM SIGKDD Explorations Newsletter 6(1), 20-29. DOI
2 
Cartiaux, F.-B., Legoll, F., Libal, A., and Reygner, J. (2024) Survival Probability of Structures under Fatigue: A Data-Based Approach. Probabilistic Engineering Mechanics 77, 103657. DOI
3 
Chen, C. L., Tsai, M. H., and Zhang, W. Y. (2025) Development of Big Data-Driven Approach for Predicting Bridge Component Deterioration: Case Study in Taiwan. Results in Engineering 28, 108175. DOI
4 
Fard, F., and Sadeghi Naieni Fard, F. (2024) Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network. Remote Sensing 16(2), 367. DOI
5 
Jeong, Y. S., Kim, W. S., Lee, I. K., and Lee, J. H. (2016a) Bridge Life Cycle Cost Analysis of Preventive Maintenance. Journal of the Korea Institute for Structural Maintenance and Inspection 20(6), 1-9. DOI
6 
Jeong, Y., Kim, W., Lee, I., Lee, J., and Kim, J. (2016b) Definition, End-of-Life Criterion and Prediction of Service Life for Bridge Maintenance. Journal of the Korea Institute for Structural Maintenance and Inspection 20(4), 68-76. DOI
7 
Kim, H. S. (2021) Deterioration Patterns and Deicing Salt Damages of Highway Bridges. Journal of the Korean Society of Road Engineers 23(4), 11-18. Google Search
8 
Kim, W., Yang, J., Min, G., and Kim, W. (2026) Long-Term Performance Prediction of Concrete Bridge Decks through Machine Learning and Survival Analysis Approaches. Journal of the Korea Concrete Institute 38(2), 147-154. DOI
9 
Lee, C., Choi, Y., and Kong, J. S. (2025) Data Augmentation and Interpretable Deep Learning for Remaining Life Prediction of Highway Bridges. Journal of Structure Research and Practice 3(2), 99-114. Google Search
10 
Lee, J., Kim, W., Min, G., and Kim, W. (2024) Development of a Predictive Model for Bridge Deck Condition Rating and Defect Index Using Various Machine Learning Algorithms. Journal of the Korea Concrete Institute 36(6), 657-664. DOI
11 
Lee, J., Min, G., and Kim, W. (2023) Development of Performance Evaluation and Prediction Models for Bridge Components Considering Time Characteristics. Journal of the Korea Concrete Institute 35(6), 673-680. DOI
12 
Lim, S., and Chi, S. (2021) Damage Prediction on Bridge Decks Considering Environmental Effects with the Application of Deep Neural Networks. KSCE Journal of Civil Engineering 25(2), 371-385. DOI
13 
Lu, G., Liu, A., Guo, W., and Zhang, X. (2022) Seismic Fragility Curves Development for Double-Column Piers of Highway Bridges Applying Cox Hazard Models of Survival Analyses. Structures 45, 2104-2116. DOI
14 
MOLIT (2026) Detailed Guidelines for Safety Inspection and Maintenance of Facilities (Safety Inspection and Diagnosis) Sejong, Korea: Ministry of Land, Infrastructure and Transport (MOLIT). Google Search
15 
Xia, H. W., Ni, Y. Q., Wong, K. Y., and Ko, J. M. (2012) Reliability-Based Condition Assessment of In-Service Bridges Using Mixture Distribution Models. Computers & Structures 106-107, 204-213. DOI
16 
Zeng, C., Huang, J., Wang, H., Xie, J., and Zhang, Y. (2023) Deep Bayesian Survival Analysis of Rail Useful Lifetime. Engineering Structures 295, 116822. DOI