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Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
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  • Korea Citation Index (KCI)

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  3. 2021-10 (Vol.25 No.5)
  4. 10.11112/jksmi.2021.25.5.135
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References

1 
NARS, (2020), The Status and future tasks of old buildings, National Assembly Research ServiceGoogle Search
2 
Park, B., Kim, D., Park, D.-W. (2020), Predictive System for Unconfined Compressive Strength of Lightweight Treated Soil(LTS) Using Deep Learning, Journal of the Korea Institute for Structural Maintenance and Inspection, 24(3), 18-25.DOI
3 
Eskandari-Naddaf, H., Kazemi, R. (2017), ANN Prediction of Cement Mortar Compressive Strength, Influence of Cement Strength Class, Construction and Building Materials, 138, 1-11.DOI
4 
Tenza-Abril, A. J., Villacampa, Y., Solak, A. M. (2018), Prediction and Sensitivity Analysis of Compressive Strength in Segregated Lightweight Concrete Based on Artificial Neural Network Using Ultrasonic Pulse Velocity, Construction and Building Materials, 189, 1173-1183.DOI
5 
Onyari, E. K., Ikotun, B. D. (2018), Prediction of Compressive and Flexural Strengths of a Modified Zeolite Additive Mortar Using Artificial Neural Network, Construction and Building Materials, 187, 1232-1241.DOI
6 
Ashrafian, A., Taheri Amiri, M. J., Rezaie-Balf, M. (2018), Prediction of Compressive Strength and Ultrasonic Pulse Velocity of Fiber Reinforced Concrete Incorporating Nano Silica Using Heuristic Regression Methods, Construction and Building Materials, 190, 479-494.DOI
7 
Cascardi, A., Micelli, F., Aiello, M. A. (2017), An Artificial Neural Networks Model for the Prediction of the Compressive Strength of FRP-Confined Concrete Circular Columns, Engineering Structures, 140, 199-208.DOI
8 
Başyiǧit, C., Comak, B., Kilincarslan, Ş. (2012), Assessment of Concrete Compressive Strength by Image Processing Technique, Construction and Building Materials, 37, 526-532.DOI
9 
Dogan, G., Arslan, M. H., Ceylan, M. (2017), Concrete Compressive Strength Detection Using Image Processing Based New Test Method, Measurement: Journal of the International Measurement Confederation, 109, 137-148.DOI
10 
Doǧan, G., Arslan, M. H., Ceylan, M. (2015), Statistical Feature Extraction Based on an Ann Approach for Estimating the Compressive Strength of Concrete, Neural Network World, 25(3), 301-318.DOI
11 
Géron, A. (2017), Hands-On Machine Learning with Scikit-Learn & TensorFlow, O’Reilly Media, Inc, California, 336-339.Google Search
12 
KS F 2405 Standard test method for compressive strength of concreteGoogle Search