- Title
- Initial state of excavated soil and rock (ESR) to influence the stabilisation with cement
- Creator
- Lu, Yi; Xu, Changhao; Baghbani, Abolfazl
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198672
- Identifier
- vital:19082
- Identifier
-
https://doi.org/10.1016/j.conbuildmat.2023.132879
- Identifier
- ISSN:0950-0618
- Abstract
- This paper investigates the initial state of excavated soil and rock (ESR). These initial states include dry density, organic content, water content (Wc), cement content (Cc), liquid index (LI), dry or wet mixing method. Three ESRs collected from tunnelling projects and kaolin were used in this study to compare. The specimens (i.e., 50 mm in diameter and 100 mm in height) were prepared in the laboratory and cured at 7 and 14 days, and then assessed by the unconfined compressive strength (UCS) test. The analysis shows that the ratio of Wc/Cc is the primary factor to obtain different UCS for high LI ESR and a simple equation is proposed for quick prediction. For ESR with a more general LI, predictive equations are also proposed in terms of artificial neural network (ANN) and genetic programming (GP) for 7-days curing time. The results indicate that the both ANN models with Bayesian Regularization (BR) algorithm outperform ANN with Levenberg-Marquardt (LM) and GP model are accurate to predict UCS of mixtures. © 2023 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- Construction and Building Materials Vol. 400, no. (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 Elsevier Ltd
- Rights
- Open Access
- Subject
- 3302 Building; 4005 Civil engineering; 4016 Materials engineering; Artificial intelligence; Cement; Excavated soil and rock; Recycling; Sustainability; Tunnel construction; Unconfined compressive strength
- Full Text
- Reviewed
- Funder
- This work was supported by the National Natural Science Foundation of China (Grant No. 52108310, 51991393, 5202010500).
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