This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination ( R 2 ) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.

中文翻译：

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预测城市住房建设土壤抗剪强度的混合计算智能方法：以 Vinhomes Imperia 项目为例，海防市（越南）

本研究提出了一种基于支持向量回归 (SVR) 和粒子群优化 (PSO) 混合的估计土壤剪切强度的替代方法。SVR 用作基于一组十二个变量（包括样品深度、砂含量、壤土含量、粘土含量、含水量、湿密度、干密度、孔隙比、液限、塑限、塑指数和液体指数。名为 PSO-SVR 的混合框架依赖 PSO 作为元启发式算法来优化所采用函数逼近器的训练阶段。从越南的一个住房项目中收集了一个由 443 个与剪切强度实验结果相关的土壤样本组成的数据集。然后使用该数据集来训练和验证专门为剪切强度估计构建的 PSO-SVR 模型的性能。混合模型取得了良好的建模结果，均方根误差 (RMSE) = 0.038，平均绝对百分比误差 (MAPE) = 9.701%，决定系数 (R 2 ) = 0.888。因此，PSO-SVR 模型可以成为参与高层住宅项目设计阶段的潜在替代方案。