Implementation of the state‐of‐the‐art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro‐mechanical behavior of unsaturated soils. In this paper, to address this objective, two artificial intelligence (AI) models were developed for estimating matric suction in unsaturated soils based on the particle swarm optimization support vector regression (PSO‐SVR) and multivariate adaptive regression spline (MARS) algorithms. The results suggest that both these models can reasonably estimate matric suction. Compared to the MARS model, the PSO‐SVR model can achieve higher accuracy. Nonetheless, the MARS model facilitates the sensitivity analysis and the selection of essential inputs. A novel integrated framework is proposed and validated, leveraging the strengths, and alleviating the limitations of the PSO‐SVR and MARS algorithms for reliable and rapid estimation of matric suction in the range of 0–1500 kPa for low plastic soils (0 < Ip ≤ 7). Six inputs are required to use this model successfully; some can be measured using conventional laboratory tests, and others can be calculated from mass‐volume relationships.

中文翻译：

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使用多种人工智能技术估计非饱和土壤基质吸力的框架

由于缺乏快速、可靠且经济的基质吸力测量技术，将最先进的非饱和土力学理解应用于岩土工程实践在一定程度上受到限制。基质吸力是显着影响非饱和土水力学行为的关键应力状态变量之一。为了实现这一目标，本文开发了两种人工智能（AI）模型，用于基于粒子群优化支持向量回归（PSO-SVR）和多元自适应回归样条（MARS）算法来估计非饱和土中的基质吸力。结果表明，这两个模型都可以合理地估计基质吸力。与MARS模型相比，PSO-SVR模型可以获得更高的精度。尽管如此，MARS 模型有助于敏感性分析和基本输入的选择。提出并验证了一种新颖的集成框架，利用 PSO-SVR 和 MARS 算法的优势并减轻其局限性，可以可靠、快速地估计低塑性土壤 0-1500 kPa 范围内的基质吸力（0 <我p≤ 7)。成功使用该模型需要六个输入；有些可以使用传统的实验室测试来测量，有些可以根据质量-体积关系来计算。