- Title
- AI grey box model for alum sludge as a soil stabilizer : an accurate predictive tool
- Creator
- Baghbani, Abolfazl; Nguyen, Minh; Kafle, Bidur; Baghbani, Hasan; Shirani Faradonbeh, Roohollah
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/196617
- Identifier
- vital:18712
- Identifier
-
https://doi.org/10.1080/19386362.2023.2258749
- Identifier
- ISSN:1938-6362 (ISSN)
- Abstract
- By using a grey box AI model, a comprehensive study is presented on the behaviour prediction of alum sludge as a soil stabilizer. To creat models for predicting the California bearing rtio (CBR) of alum sludge as a soil stabilizer, the study employs statistical models, including multiple linear regression (MLR) and Partial least squares (PLS), and advanced artificial intelligence, including classificatoin and regression random forests (CRRF) and classification and regression trees (CART). Results show that CRRF and CART models accurately predict CBR values better than MLR and PLS models. For predicting the behaviour of alum sludge in soil stablization, the compaction number of hammer and sludge content were the most significant parameters. Gs and optimum moisture content of soil were the least important parameters. Study results provide valuable insights into alum sludge’s behaviour as a soil stablizer, which could reduce waste and promote sustainable practice. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
- Publisher
- Taylor and Francis Ltd.
- Relation
- International Journal of Geotechnical Engineering Vol. 17, no. 5 (2023), p. 480-494
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group
- Subject
- 4005 Civil engineering; 4019 Resources engineering and extractive metallurgy; AI; Alum sludge; Recycling; Soil stabiliser; Sustainability; Water treatment sludge
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