The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm
Open accessAbstract
Abstract The prediction results of high-performance concrete compressive strength (HPCCS) based on machine learning methods are seriously influenced by input variables and model parameters. This study proposes a method with two stages to select proper variables, simplify parameter settings, and predict HPCCS. The appropriate variables are selected in the first stage by measuring their importance based on random forest, and then are optimized to predict HPCCS in the second stage. The results show that the proposed method was effective for input variable optimization, and could return better predictions than that without variable optimization, provided that the parameters are set within a reasonable range. Compared with previous models, the proposed method shows a strong generalization capacity for HPCCS prediction. We find that the prediction performance of the model is better when the input variables are expressed as absolute mass, and the model performers well when the actual compressive strength of HPC is high.
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Cites
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Cited by
Machine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
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Farooq, F., Ahmed, W., Akbar, A., Aslam, F., & Alyousef, R. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126032 · Cited by 425
Mohtasham Moein, M., Saradar, A., Rahmati, K., Ghasemzadeh Mousavinejad, S. H., Bristow, J., Aramali, V., & Karakouzian, M. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105444 · Cited by 391
Machine learning in concrete science: applications, challenges, and best practices
Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388
Metadata
- Authors
- Han, Q., Gui, C., Xu, J., & Lacidogna, G.
- Venue
- Construction and Building Materials
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 226
- Pages
- 734-742
- ISSN
- 0950-0618 (ISSN-L)
- OpenAlex
- W2964938350
- Cited by
- 408
- References
- 28
- FWCI
- 22.61
- Type
- journal article
- Language
- en
- Open access
- green
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- Han, Q., Gui, C., Xu, J., & Lacidogna, G. (2019). A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2019.07.315
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