JKCI - Journal of the Korea Concrete Institute

1

Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., and Inman, D. J. (2017) Real-Time Vibration-Based Structural Damage Detection Using One-Dimensional Convolutional Neural Networks. Journal of Sound and Vibration 388, 154-170.

2

Abuhmida, M., Milner, D., and Bai, J. (2023) Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network. IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17(2), 171-182.

3

Al Fuhaid, A. F., and Alanazi, H. (2023) Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms. Materials 16(3), 1277.

4

Altuncı, Y. T. (2024) A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models. Buildings 14(12), 3851.

5

Amlashi, A. T., Alidoust, P., Pazhouhi, M., Niavol, K. P., Khabiri, S., and Ghanizadeh, A. R. (2021) AI-Based Formulation for Mechanical and Workability Properties of Eco-Friendly Concrete Made by Waste Foundry Sand. Journal of Materials in Civil Engineering 33(4), 04021031.

6

Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., and Inman, D. J. (2021) A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications. Mechanical Systems and Signal Processing 147, 107077.

7

Barreto, L. M., and Salvador Filho, J. A. A. (2024) Applications of Digital Twins in Reinforced and Prestressed Concrete Bridge Infrastructure. Procedia Structural Integrity 64, 1168-1175.

8

Buswell, R. A., Leal de Silva, W. R., Jones, S. Z., and Dirrenberger, J. (2018) 3D Printing Using Concrete Extrusion: A Roadmap for Research. Cement and Concrete Research 112, 37-49.

9

Cha, Y. J., Choi, W., and Büyüköztürk, O. (2017) Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Computer-Aided Civil and Infrastructure Engineering 32(5), 361-378.

10

Chou, J. S., Tsai, C. F., Pham, A. D., and Lu, Y. H. (2014) Machine Learning in Concrete Strength Simulations: Multi-Nation Data Analytics. Construction and Building Materials 73, 771-780.

11

Dung, C. V., and Anh, L. D. (2019) Autonomous Concrete Crack Detection Using Deep Fully Convolutional Neural Network. Automation in Construction 99, 52-58.

12

Farrar, C. R., and Worden, K. (2012) Structural Health Monitoring: A Machine Learning Perspective. John Wiley and Sons

13

Gamil, Y. (2023) Machine Learning in Concrete Technology: A Review of Current Researches, Trends, and Applications. Frontiers in Built Environment 9.

14

Gharib, S., and Moselhi, O. (2023) A Review of Computer Vision-Based Techniques for Construction Progress Monitoring. Chennai, India; 40th International Symposium on Automation and Robotics in Construction (IAARC) 529-536.

15

Guan, S., Liu, H., Pourreza, H. R., and Mahyar, H. (2021) Deep Learning Approaches in Pavement Distress Identification: A Review. arXiv e-prints arXiv:2308.00828

16

Han, X., Zhao, Z., Chen, L., Hu, X., Tian, Y., Zhai, C., Wang, L., and Huang, X. (2022) Structural Damage-Causing Concrete Cracking Detection Based on a Deep-Learning Method. Construction and Building Materials 337, 127562.

17

Hu, X. (2021) Progress in Artificial Intelligence-Based Prediction of Concrete Performance. Journal of Advanced Concrete Technology 19(8), 924-936.

18

Islam, M. M. M., and Kim, J. M. (2019) Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network. Sensors 19(19), 4251.

19

Kazemi, R. (2023) Artificial Intelligence Techniques in Advanced Concrete Technology: A Comprehensive Survey on 10 Years Research Trend. Engineering Reports 5(9), e12676.

20

Kazemi, R. (2024) A Hybrid Artificial Intelligence Approach for Modeling the Carbonation Depth of Sustainable Concrete Containing Fly Ash. Scientific Reports 14, 11948.

21

Khan, A. Q., Muhammad, S. G., Raza, A., and Pimanmas, A. (2026) Machine Learning Models for Predicting Carbonation Depth in Fly Ash Concrete: Performance and Interpretability Insights. Journal of Road Engineering 6(1), 74-90.

22

Khan, K., Amin, M. N., Sahar, U. U., Ahmad, W., Shah, K., and Mohamed, A. (2022) Machine Learning Techniques to Evaluate the Ultrasonic Pulse Velocity of Hybrid Fiber-Reinforced Concrete Modified with Nano-Silica. Frontiers in Materials 9, 1098304.

23

Kicinger, R., Arciszewski, T., and De Jong, K. (2005) Evolutionary Computation and Structural Design: A Survey of the State of the Art. Computers & Structures 83(23-24), 1943-1978.

24

Liang, L., Liu, M., Martin, C., and Sun, W. (2018) A Deep Learning Approach to Estimate Stress Distribution: A Fast and Accurate Surrogate of Finite-Element Analysis. Journal of the Royal Society Interface 15, 20170844.

25

Lundberg, S. M., and Lee, S. I. (2017) A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30, 4765-4774.

26

Madubuike, O. C., Anumba, C. J., and Khallaf, R. (2022) A Review of Digital Twin Applications in Construction. Journal of Information Technology in Construction 22, 147-172.

27

Mangalathu, S., Hwang, S. H., and Jeon, J. S. (2020) Failure Mode and Effects Analysis of RC Members Based on Machine Learning-Based Shapley Additive Explanations (SHAP) Approach. Engineering Structures 219.

28

Marani, A., Zhang, L., and Nehdi, M. L. (2023) Design of Concrete Incorporating Microencapsulated Phase Change Materials for Clean Energy: A Ternary Machine Learning Approach Based on Generative Adversarial Networks. Engineering Applications of Artificial Intelligence 118, 105652.

29

Mehta, P. K., and Monteiro, P. J. (2017) Concrete: Microstructure, Properties, and Materials (4th ed.). McGraw-Hill Education

30

Min, H., and Kim, H. (2025) Machine-Learning-Based Concrete Strength Prediction Considering Effect of High-Temperature Exposure and Supplementary Cementitious Material (SCM) Content. Journal of the Korea Concrete Institute 37(1), 37-47.

31

Mirzaei, A. (2025) A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction. Mathematical and Computational Applications 30, 83.

32

Oviedo, A. I., Londoño, J. M., Vargas, J. F., Zuluaga, C., and Gómez, A. (2024) Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms. Modelling 5(3), 642-658.

33

Prakash, V., Debono, C. J., Musarat, M. A., Borg, R. P., Seychell, D., Ding, W., and Shu, J. (2025) Structural Health Monitoring of Concrete Bridges through Artificial Intelligence: A Narrative Review. Applied Sciences 15(9), 4588.

34

Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135-1144.

35

Su, C., and Wang, W. (2020) Concrete Cracks Detection Using Convolutional Neural Network Based on Transfer Learning. Mathematical Problems in Engineering 2020, 7240129.

36

Topçu, İ. B., and Saridemir, M. (2008) Prediction of Compressive Strength of Concrete Containing Fly Ash Using Artificial Neural Networks and Fuzzy Logic. Computational Materials Science 41(3), 305-311.

37

Wang, T., Altabey, A. A., Noori, M., and Ghiasi, R. (2020) Deep Learning Based Approach for Response Prediction of Beam-Like Structures. Structural Durability & Health Monitoring 14(4), 315-338.

38

Wang, Z., and Cha, Y. J. (2021) Unsupervised Deep Learning Approach Using a Deep Auto-Encoder with a One-Class Support Vector Machine to Detect Damage. Structural Health Monitoring 20(1), 406-425.

39

Wu, Y., and Zhou, Y. (2022) Splitting Tensile Strength Prediction of Sustainable High-Performance Concrete Using Machine Learning Techniques. Environmental Science and Pollution Research 29(59), 89198-89209.

40

Yeh, I. C. (1998) Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks. Cement and Concrete Research 28(12), 1797-1808.

41

Zhang, F., Wang, L., and Wang, Z. (2022) Toward Fully-Automated Code Compliance Checking of Building Regulations: Challenges for Rule Interpretation and Representation. European Conference on Computing in Construction Ixia, Rhodes, Greece

JKCIJournal of
the Korea Concrete Institute

Journal of the Korea Concrete Institute

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JKCIJournal ofthe Korea Concrete Institute