基于自适应采样和代理模型的滑带土参数反演 English Version
Parameter Inversion of Slip Zone Soil Based on Adaptive Sampling and Surrogate Models
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摘要: 针对当前反演中为代理模型构建训练样本时采样方法的不足，引入CV-Voronoi自适应序列采样法进行训练样本的生成，采用麻雀搜索算法（SSA）优化孪生支持向量回归（TSVR）模型得到SSA-TSVR代理模型，以SSA作为优化算法，提出新的反演技术。以白水河滑坡滑带土抗剪强度参数反演为例对新反演方法进行了工程应用验证，比较了不同样本生成方法（自适应序列采样法、正交设计和均匀设计）及代理模型（SVR、TSVR、SSA-SVR及SSA-TSVR）对反演结果的影响。结果表明自适应序列采样方法有明显优势，使得反演误差降低了一半以上，其不仅能够显著改善反演效果，还能够用更少样本达到更高的精度。采用的SSA-TSVR代理模型具有更高的反演精度和计算速度，为岩土工程力学参数反演提供了一种新的思路。
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关键词:
- 强度参数 /
- 自适应序列采样 /
- 反演 /
- SSA-TSVR代理模型 /
- 滑带土
Abstract: To solve the shortcomings of sampling methods for constructing training dataset of surrogate models in current inverse analysis, this study introduces the CV-Voronoi adaptive sequential sampling method for generating training samples. Moreover, the sparrow search algorithm (SSA) is employed to optimize the twin support vector regression (TSVR) model, and the SSA-TSVR surrogate model is then generated. Based on the adaptive sequential sampling method and SSA-TSVR model, using SSA as the optimization algorithm, a new inversion technique is proposed. Using the shear strength parameter inversion of the Baishui River landslide slip zone soil as an example, the new inversion method was validated through engineering application. The effects of different sample generation methods (adaptive sequential sampling, orthogonal design, and uniform design) and surrogate models (SVR, TSVR, SSA-SVR, and SSA-TSVR) on the inversion results were compared. The results showed that the adaptive sequential sampling method has a significant advantage, reducing the inversion error by more than half. This method not only significantly improves the inversion performance, but also achieves higher accuracy with fewer samples. The SSA-TSVR surrogate model offers higher inversion accuracy and computational speed, providing a new approach for inversion analysis of geotechnical engineering mechanical parameters.
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