The use of cement as a curing agent has been widely adopted in soft soil engineering to increase the strength of soft soil. The cemented soil is gradually exposed to the air and in a natural environment becomes unsaturated. Unconfined compressive strength (UCS) of the unsaturated cemented soils is a key parameter for assessing their strength behaviour. UCS determination of unsaturated cemented soils by using laboratory methods is a complex, time-consuming, and expensive procedure due to the difficulty in suction control. This study aims to model the UCS of unsaturated cemented Wenzhou clay, i.e., capture the nonlinear relations between UCS and its influential variables including cement content (%), dry density (g/cm³) and suction (MPa) for the first time by using machine learning approach. Toward this aim, three advanced computational frameworks are developed based on hybrid evolutionary approaches in which evolutionary optimisation algorithms including genetic algorithm (GA), particle swarm optimisation (PSO) and imperialist competitive algorithm (ICA) are hybridised with artificial neural network (ANN). Results show that developed models have a great ability to mimic the nonlinear relationships between UCS and its influential variables and PSO-ANN presents the best performance among three models on the training dataset with $R^2=0.9888$, $\mathit{RMSE}=0.129$ and $\mathit{VAF}=97.742$, and testing dataset with $R^2=0.9412$, $\mathit{RMSE}=0.237$ and $\mathit{VAF}=90.414$. To facilitate engineering application, an engineering database for Wenzhou soft clay at different cement ratios (up to 11%), suctions (up to 300 MPa) and dry densities (1–1.5 g/cm³) is built by using the developed PSO-ANN model.

使用水泥作为固化剂在软土工程中被广泛采用，以增加软土的强度。水泥土逐渐暴露在空气中，在自然环境中变得不饱和。不饱和胶结土的无侧限抗压强度 (UCS) 是评估其强度行为的关键参数。由于吸力控制困难，使用实验室方法测定非饱和胶结土的 UCS 是一个复杂、耗时且昂贵的过程。本研究旨在模拟非饱和胶结温州粘土的 UCS，即捕捉 UCS 与其影响变量之间的非线性关系，包括水泥含量 (%)、干密度 (g/cm ³) 和吸力 (MPa) 首次使用机器学习方法。为此，基于混合进化方法开发了三种先进的计算框架，其中包括遗传算法 (GA)、粒子群优化 (PSO) 和帝国主义竞争算法 (ICA) 在内的进化优化算法与人工神经网络 (ANN) 混合。结果表明，开发的模型具有很好的模拟 UCS 与其影响变量之间的非线性关系的能力，PSO-ANN 在训练数据集上的三个模型中表现最好${\mathrm{电阻}}^2=0.9888$, $\mathit{均方根误差}=0.129$ 和 $\mathit{变频调速器}=97.742$, 和测试数据集 ${\mathrm{电阻}}^2=0.9412$, $\mathit{均方根误差}=0.237$ 和 $\mathit{变频调速器}=90.414$. 为便于工程应用，利用开发的 PSO- 建立了不同水泥比（高达 11%）、吸力（高达 300 MPa）和干密度（1-1.5 g/cm ³）的温州软粘土工程数据库。神经网络模型。