Mobile QR Code QR CODE

Journal of the Korea Concrete Institute

ISO Journal TitleJ Korea Concr Inst.
  • SCOPUS
  • KCI Accredited Journal

Journal Search

  1. Home
  2. All Issues
  3. 2026-04 (Vol.38 No.2)
  4. 10.4334/JKCI.2026.38.2.147
Export citation EndNote
XML PDF INFO REF

References

1 
Cartiaux, F.-B., Legoll, F., Libal, A., and Reygner, J. (2024a) Survival Probability of Structures under Fatigue: A Data-Based Approach. Probabilistic Engineering Mechanics 77, 103657. DOI
2 
Cartiaux, F.-B., Legoll, F., Libal, A., Reygner, J., and Semiao, J. (2024b) Application of a New Method for Probabilistic Fatigue Analysis from Strain Measurements on Bridges. Proceedings of the 11th European Workshop on Structural Health Monitoring (EWSHM 2024) Potsdam, Germany: June 2024 DOI
3 
Gerds, T. A., and Schumacher, M. (2006) Consistent Estimation of the Expected Brier Score in General Survival Models with Right-Censored Event Times. Biometrical Journal 48(6), 1029-1040. DOI
4 
Goyal, R. (2020) Bridge Condition Rating Forecast—Survival-Based Models. Available online. Accessed 16 April 2026 Google Search
5 
Graf, E., Schmoor, C., Sauerbrei, W., and Schumacher, M. (1999) Assessment and Comparison of Prognostic Classification Schemes for Survival Data. Statistics in Medicine 18(17-18), 2529-2545. DOI
6 
Jeong, Y., Kim, W., Lee, I., and Lee, J. (2016a) Bridge Life Cycle Cost Analysis of Preventive Maintenance. Journal of the Korea Institute for Structural Maintenance and Inspection 20(6), 1-9. (In Korean) DOI
7 
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. (In Korean) DOI
8 
Jeong, Y., Lee, I., and Kim, W. (2024) Deterioration Mechanism and Repair Methods for Concrete Decks in Bridges. Journal of the Korea Concrete Institute 36(4), 347-355. (In Korean) DOI
9 
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. (In Korean) DOI
10 
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. (In Korean) DOI
11 
Li, Y., Jia, C., Chen, H., Su, H., Chen, J., and Wang, D. (2023) Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features. Sustainability 15(18), 13847. DOI
12 
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
13 
Nabizadeh, A., Tabatabai, H., and Tabatabai, M. A. (2020) Conditional Survival Analysis for Concrete Bridge Decks. Life Cycle Reliability and Safety Engineering 9, 63-75. DOI
14 
Ozturk, B., Hussein, A. F., and El Naggar, M. H. (2025) Machine Learning-Based Seismic Damage Assessment of a Bridge Portfolio in Cohesive Soil. Buildings 15(10), 1682. DOI
15 
Royston, P., and Sauerbrei, W. (2004) A New Measure of Prognostic Separation in Survival Data. Statistics in Medicine 23, 723-748. DOI
16 
Sanyal, P., and Dalui, S. K. (2024) Computational Fluid Dynamics and Artificial Neural Network-Based Analysis and Forecasting of Wind Effects on Obliquely Parallel Multiple Building Models Using Categorical Variable Encoding. The Structural Design of Tall and Special Buildings 33(8), e2105. DOI
17 
Schultz, C., McNinch, C., Qi, J., Smith, M., and Barclay, N. (2023) Culvert Condition Prediction via Artificial Neural Network Machine Learning-Based Models Using SMOTE. Proceedings of IEEE SoutheastCon 2023 Orlando, FL, USA: April 1-16, 2023 DOI
18 
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., and Kattan, M. W. (2010) Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures. Epidemiology 21(1), 128-138. DOI
19 
Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J., and Tibshirani, R. J. (2012) Strong Rules for Discarding Predictors in Lasso-Type Problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74(2), 245-266. DOI
20 
Wilson, C. M., Li, K., Sun, Q., Kuan, P. F., and Wang, X. (2021) Fenchel Duality of Cox Partial Likelihood with an Application in Survival Kernel Learning. Artificial Intelligence in Medicine 116, 102077. DOI
21 
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 112-113, 245-257. DOI
22 
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