Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio
Created by W.Langdon from
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- @Article{baghbani:2023:AS,
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author = "Abolfazl Baghbani and Minh Duc Nguyen and
Ali Alnedawi and Nick Milne and Thomas Baumgartl and
Hossam Abuel-Naga",
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title = "Improving Soil Stability with Alum Sludge: An
{AI-Enabled} Approach for Accurate Prediction of
California Bearing Ratio",
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journal = "Applied Sciences",
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year = "2023",
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volume = "13",
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number = "8",
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pages = "Article No. 4934",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/13/8/4934",
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DOI = "doi:10.3390/app13084934",
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abstract = "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.",
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notes = "also known as \cite{app13084934}",
- }
Genetic Programming entries for
Abolfazl Baghbani
Minh Duc Nguyen
Ali Alnedawi
Nick Milne
Thomas Baumgartl
Hossam Abuel-Naga
Citations