Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio
نویسندگان
چکیده
Alum sludge is a byproduct of water treatment plants, and its use as soil stabilizer has gained increasing attention due to economic environmental benefits. Its application been shown improve the strength stability soil, making it suitable for various engineering applications. However, go beyond just measuring effects alum stabilizer, this study investigates potential artificial intelligence (AI) methods predicting California bearing ratio (CBR) soils stabilized with sludge. Three AI methods, including two black box (artificial neural network support vector machines) one grey method (genetic programming), were used predict CBR, based on database nine input parameters. The results demonstrate effectiveness in CBR good accuracy (R2 values ranging from 0.94 0.99 MAE 0.30 0.51). Moreover, novel approach, using genetic programming, produced an equation that accurately estimated incorporating seven inputs. analysis parameter sensitivity importance, revealed number hammer blows compaction was most important parameter, while parameters maximum dry density mixture least important. This highlights useful tool performance stabilizer.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13084934