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Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

Version 2 2024-06-02, 22:36
Version 1 2023-05-29, 04:34
journal contribution
posted on 2024-06-02, 22:36 authored by Abolfazl BaghbaniAbolfazl Baghbani, MD Nguyen, A Alnedawi, Nick MilneNick Milne, T Baumgartl, H Abuel-Naga
Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.

History

Journal

Applied Sciences (Switzerland)

Volume

13

Article number

ARTN 4934

Pagination

1-26

Location

Basel, Switzerland

ISSN

2076-3417

eISSN

2076-3417

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

8

Publisher

MDPI