Soil seismic liquefaction discrimination model based on support vector machine

FAN Meng; YANG Zheng-quan; LI Jing-jun; LIU Xiao-sheng; ZHAO Jian-ming

doi:10.16285/j.rsm.2025.0282

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Soil seismic liquefaction discrimination model based on support vector machine

更新时间：2026-06-25

- Soil seismic liquefaction discrimination model based on support vector machine
- Vol. 46, Issue 12, Pages: 3985-3997+4028(2025)
- 作者机构：
  
  1. 中国水利水电科学研究院流域水循环与水安全全国重点实验室,北京,100038
  2. 水利部水工程抗震与应急支持工程技术研究中心,北京,100048
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2025.0282
  CLC： TU435
- Online First：20 December 2025，
  
  Published：2025
- 稿件说明：
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FAN Meng, YANG Zheng-quan, LI Jing-jun, et al. Soil seismic liquefaction discrimination model based on support vector machine[J]. 2025, 46(12): 3985-3997+4028.
DOI：

FAN Meng, YANG Zheng-quan, LI Jing-jun, et al. Soil seismic liquefaction discrimination model based on support vector machine[J]. 2025, 46(12): 3985-3997+4028. DOI： 10.16285/j.rsm.2025.0282.

摘要

土体液化可能性评价是砂土液化研究的重要任务之一。基于现场液化调查案例，通过灰色关联度法给出了各因素与液化之间的关联度，并从地震动荷载、土体环境和土体性质3个方面分析了各因素与液化之间的关系。采用支持向量机算法和普拉特缩放原理构建了砂土液化判别概率模型，与现有的确定性和概率性液化判别方法进行了对比研究，验证了模型的有效性。将模型应用于实际工程案例，探究了模型的实用性。结果表明：各因素与液化有明显的关联关系且影响关系复杂。建立的支持向量机土体液化判别模型对测试集与验证集的总体判别精度分别达到了89.41%、86.67%，与现有液化判别方法的判别结果相比，具有较好的判别精度。模型拓宽了土体液化判别的适用深度，同时可给出待评价土体的液化概率。研究成果可为土体地震液化概率评价提供一定的参考。

Abstract

One of the important tasks in the study of sand liquefaction is to assess soil liquefaction likelihood. Based on the field liquefaction investigation cases

the correlation degree between each factor and liquefaction was given by grey relational degree method

and the relationships between factors from seismic load

soil environment

soil properties and liquefaction were analyzed. The support vector machine (SVM) algorithm and Platt scaling principle were then used to construct a probabilistic model for liquefaction assessment

and comparative study was conducted with existing deterministic and probabilistic liquefaction assessment methods to analyze its effectiveness. The model was then applied to actual engineering cases and its practicality was explored finally. The results indicate that it has a significant correlation with liquefaction and the influence relationship between each factor and liquefaction is complex. The overall classification accuracy of the SVM-based soil liquefaction assessment model was 89.41% for the test set and 86.67% for the validation set

higher than that of existing liquefaction assessment methods. The depth range of applicability is extended

and the model can also provide the soil liquefaction probability. The results provide a reference for probabilistic assessment of soil seismic liquefaction.

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