Author:
Wang Weiwei,Zhao Long,Dong Daoliang
Publisher
Springer Science and Business Media LLC
Reference48 articles.
1. Tja, T.: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Eng. Softw. 41, 886–892 (2010)
2. Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P., Lourenço, P.B.: Soft computing techniques for the prediction of concrete compressive strength using non-destructive tests. Constr. Build. Mater. 303, 124450 (2021)
3. Bhatt, S., Jain, P.K., Pradesh, M.: Prediction of California bearing ratio of soils using artificial neural network. Am. Int. J. Res. Sci. Technol. Eng. Math. 8, 156–161 (2014)
4. Ahmed, M., AlQadhi, S., Mallick, J., Ben, K.N., Le, H.A., Singh, C.K., et al.: Artificial neural networks for sustainable development of the construction industry. Sustainability 14, 14738 (2022)
5. Ebid, A.M.: 35 Years of (AI) in geotechnical engineering: state of the art. Geotech. Geol. Eng. 39, 637–690 (2021)