Open Access
Issue
E3S Web Conf.
Volume 237, 2021
3rd International Symposium on Architecture Research Frontiers and Ecological Environment (ARFEE 2020)
Article Number 03033
Number of page(s) 5
Section Architecture Science and Civil Engineering
DOI https://doi.org/10.1051/e3sconf/202123703033
Published online 09 February 2021
  1. Sun, Y., et al., A new mixture design methodology based on the Packing Density Theory for high performance concrete in bridge engineering. Construction and Building Materials, 182(2018) [Google Scholar]
  2. Amini, K., et al., Development of Prediction Models for Mechanical Properties and Durability of Concrete Using Combined Nondestructive Tests. Journal of Materials in Civil Engineering, 31(2019) [Google Scholar]
  3. Lin, S., H. Li, and H. Zhang, Experimental Study on Frost Resistance Durability and Service Life Prediction of Normal Cement Concrete. Advanced Materials Research, 368-373: p. 2425-2429 (2011) [Google Scholar]
  4. Ji, T., T. Lin, and X. Lin, A mortar mix proportion design algorithm based on artificial neural networks. Cement and Concrete Research, 36: p. 1399-1408 (2006) [Google Scholar]
  5. Yan, K. and C. Shi, Prediction of elastic modulus of normal and high strength concrete by support vector machine. Construction and Building Materials CONSTR BUILD MATER, 24: p. 1479-1485 (2010) [Google Scholar]
  6. Behforouz, B., P. Memarzadeh, and M. Eftekhar, Regression and ANN models for durability and mechanical characteristics of waste ceramic powder high performance sustainable concrete. Computers and Concrete, 25(2020) [Google Scholar]
  7. Zhang, P., A Novel Feature Selection Method based on Global Sensitivity Analysis with Application in Machine Learning-based Prediction Model. Applied Soft Computing, (2019) [PubMed] [Google Scholar]
  8. Breiman, L., Random Forests. Machine Learning, 45(1): p. 5-32 (2001) [Google Scholar]
  9. García-Pedrajas, N., C. Martínez, and J. Muñoz-Pérez, Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks). Neural networks : the official journal of the International Neural Network Society, 15: p. 1259-78 (2003) [Google Scholar]
  10. Genuer, R., J. M. Poggi, and C. Tuleau-Malot, Variable selection using random forests: Elsevier Science Inc (2010) [Google Scholar]
  11. Hyndman, R. and A. Koehler, Another look at measures of forecast accuracy. International Journal of Forecasting, 22: p. 679-688 (2006) [Google Scholar]
  12. Boddy, R. and G. Smith, Statistical Methods in Practice: for Scientists and Technologists (2009) [Google Scholar]
  13. Kwan, A., Concrete mix design based on water film thickness and paste film thickness. Cement and Concrete Composites, 39: p. 33–42 (2013) [Google Scholar]
  14. Kalla, P., et al., Mechanical and durability studies on concrete containing wollastonite–fly ash combination. Construction and Building Materials, 40: p. 1142-1150 (2013) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.