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Study on the probabilistic assessment model of sand liquefaction based on logistic regression algorithm
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Jian LI1, Jingjun LI2, 3, Meng FAN2, 3, Kaibin ZHU2, 3, Zhengquan YANG2, 3
Journal of China Institute of Water Resources and Hydropower Research | 2026, 24(3) : 271 - 284
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Journal of China Institute of Water Resources and Hydropower Research | 2026, 24(3): 271-284
Study on the probabilistic assessment model of sand liquefaction based on logistic regression algorithm
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Jian LI1, Jingjun LI2, 3, Meng FAN2, 3, Kaibin ZHU2, 3, Zhengquan YANG2, 3
Affiliations
  • 1Power China Chengdu Engineering Corporation Limited,Chengdu610072,China
  • 2China Institute of Water Resources and Hydropower Research,State Key Laboratory of Water Cycle and Water Security,Beijing100038,China
  • 3Engineering Research Center on Anti-Earthquake and Emergency Support Techniques of Hydraulic Projects,Ministry of Water Resources,Beijing100048,China
Published: 2026-05-28 doi: 10.13244/j.cnki.jiwhr.20250257
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Sand liquefaction caused by strong earthquakes is receiving increasing attention due to the frequency of extreme seismic events. The liquefaction possibility assessment is the primary task in the study of sand liquefaction. In this paper,a probability assessment model is established based on the field investigation of liquefaction cases,combined with the knowledge of probability statistics and logistic regression algorithm. The effectiveness of the model is verified by comparing with the existing deterministic liquefaction assessment methods. Furthermore,the parameters analysis affecting the liquefaction assessment results is also conducted. The results show that the liquefaction discrimination model established in this paper has a success rate of 85.70% and 82.50% for rejudging the liquefaction and non-liquefaction cases; and a success rate of 88.00% and 72.00% for the discrimination of the validation set, demonstrating a good discrimination success rate. The fine particle content, overburden stress correction factor,the correction coefficient for overburden stress,and the adjustment coefficient for seismic magnitude should be applied to correct case data when applying this model to assess liquefaction potential,which can improve the accuracy of sand liquefaction assessment.At the same time, the model can provide specific discrimination formulas. In the future, when new samples are incorporated, the model can be further improved by adjusting and modifying based on various parameters.

earthquake  /  sand liquefaction  /  logistic regression algorithm  /  liquefaction assessment  /  probability model
Jian LI, Jingjun LI, Meng FAN, Kaibin ZHU, Zhengquan YANG. Study on the probabilistic assessment model of sand liquefaction based on logistic regression algorithm[J]. Journal of China Institute of Water Resources and Hydropower Research, 2026 , 24 (3) : 271 -284 . DOI: 10.13244/j.cnki.jiwhr.20250257
Year 2026 volume 24 Issue 3
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doi: 10.13244/j.cnki.jiwhr.20250257
  • Receive Date:2025-10-28
  • Online Date:2026-06-25
  • Published:2026-05-28
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  • Received:2025-10-28
Affiliations
    1Power China Chengdu Engineering Corporation Limited,Chengdu610072,China
    2China Institute of Water Resources and Hydropower Research,State Key Laboratory of Water Cycle and Water Security,Beijing100038,China
    3Engineering Research Center on Anti-Earthquake and Emergency Support Techniques of Hydraulic Projects,Ministry of Water Resources,Beijing100048,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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