Comparative study of machine learning methods to predict compressive strength of high-performance concrete and model validation on experimental data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00836-6.pdf
Reference50 articles.
1. Al-Shamiri, A. K., Kim, J. H., Yuan, T. F., & Yoon, Y. S. (2019). Modeling the compressive strength of high-strength concrete: An extreme learning approach. Construction and Building Materials, 208, 204–219. https://doi.org/10.1016/j.conbuildmat.2019.02.165
2. Amario, M., Rangel, C. S., Pepe, M., & Toledo Filho, R. D. (2017). Optimization of normal and high strength recycled aggregate concrete mixtures by using packing model. Cement and Concrete Composites, 84, 83–92. https://doi.org/10.1016/j.cemconcomp.2017.08.016
3. Bansal, T., Talakokula, V., & Mathiyazhagan, K. (2021). Equivalent structural parameters based non-destructive prediction of sustainable concrete strength using machine learning models via piezo sensor. Measurement, 187, 110202. https://doi.org/10.1016/j.measurement.2021.110202
4. Bansal, T., Talakokula, V., & Sathujoda, P. (2022a). Machine learning-based monitoring and predicting the compressive strength of different blended cementitious systems using embedded piezo-sensor data. Measurement, 205, 112204. https://doi.org/10.1016/j.measurement.2022.112204
5. Bansal, T., Talakokula, V., & Sathujoda, P. (2022b). A machine learning approach for predicting the electro-mechanical impedance data of blended RC structures subjected to chloride laden environment. Smart Materials and Structures. https://doi.org/10.1088/1361-665X/ac3d6f
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