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Prediction and Uncertainty of Soil Compaction Parameters Based on Explicit Machine Learning Model
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Jian-wang LI1, Wen-rui QI1, Xin-yuan DING1, Hang-yu ZHOU1, Ye LIU2, *, Su QIN2, Liang-fu XIE2
Science Technology and Engineering | 2025, 25(19) : 8207 - 8217
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Science Technology and Engineering | 2025, 25(19): 8207-8217
Papers∙Architectural Science
Prediction and Uncertainty of Soil Compaction Parameters Based on Explicit Machine Learning Model
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Jian-wang LI1, Wen-rui QI1, Xin-yuan DING1, Hang-yu ZHOU1, Ye LIU2, *, Su QIN2, Liang-fu XIE2
Affiliations
  • 1 Urban Rail Transit Engineering Company Limited of China Railway 15th Bureau, Guangzhou 510400, China
  • 2 College of Civil Engineering and Architecture, Xinjiang University, Urumqi 830017, China
Published: 2025-07-08 doi: 10.12404/j.issn.1671-1815.2405717
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The accurate prediction of soil compaction parameters has practical significance for improving soil bearing capacity and reducing compressibility in geotechnical engineering. The existing models have certain limitations in prediction progress and engineering applicability, and ignore the quantification of model prediction uncertainty. Genetic programming (GP) was used to model and predict two important soil compaction parameters (optimal water content and maximum dry density) for 226 groups of soil compaction test data with extensive and representativeness. The optimal display models of optimal water content and maximum dry density were obtained respectively, and the prediction results were compared with the results of existing prediction models. The GP model was quantified by combining quantile regression method and uncertainty statistics. The results show that the compaction parameters are most affected by fine grain content and plastic limit, while the gravel content and liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content and plastic limit, while the gravel content (CG) and the liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content (CF) and the plastic limit in the soil. In addition, the quantile regression (QR) method provides 90 % confidence and the mean prediction interval (MPI) is less than 0.3.At the same time, most of the data fall within the range of uncertain bands, indicating that the GP algorithm has strong prediction ability and high prediction accuracy. This interpretable display model is more convenient for engineering applications.

optimum moisture content  /  maximum dry density  /  genetic coding algorithm  /  prediction  /  uncertainty analysis
Jian-wang LI, Wen-rui QI, Xin-yuan DING, Hang-yu ZHOU, Ye LIU, Su QIN, Liang-fu XIE. Prediction and Uncertainty of Soil Compaction Parameters Based on Explicit Machine Learning Model[J]. Science Technology and Engineering, 2025 , 25 (19) : 8207 -8217 . DOI: 10.12404/j.issn.1671-1815.2405717
Year 2025 volume 25 Issue 19
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doi: 10.12404/j.issn.1671-1815.2405717
  • Receive Date:2024-07-30
  • Online Date:2025-12-22
  • Published:2025-07-08
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  • Received:2024-07-30
  • Revised:2024-12-23
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    1 Urban Rail Transit Engineering Company Limited of China Railway 15th Bureau, Guangzhou 510400, China
    2 College of Civil Engineering and Architecture, Xinjiang University, Urumqi 830017, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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