Shear strength is a crucial property of soils regarded as its intrinsic capacity to resist failure when forces act on the soil mass. This study proposes an advanced meta-leaner to discern the shear strength property and generate a reliable estimation of the ultimate shear strength of the soil. The proposed model is named as metaheuristic-optimized meta-ensemble learning model (MOMEM) and aims at helping geotechnical engineers accurately predict the parameter of interest. The MOMEM was established with the integration of the artificial electric field algorithm (AEFA) to dynamically blend the radial basis function neural network (RBFNN) and multivariate adaptive regression splines (MARS). In the framework of forming MOMEM, the AEFA consistently monitor the learning phases of the RBFNN and MARS in mining soil shear strength property through optimizing their controlling parameters, including neuron number, Gaussian spread, regularization coefficient, and kernel function parameter. Simultaneously, RBFNN and MARS are stacked via a linear combination method with dynamic weights optimized by the AEFA metaheuristic. The one-tail t test on 20 running times affirmed that with the greatest mean and standard deviation of RMSE (mean = 0.035 kg/cm2; Std. = 0.005 kg/cm2), MAE (mean = 0.026 kg/cm2; Std. = 0.004 kg/cm2), MAPE (mean = 7.9%; Std. = 1.72%), and R2 (mean = 0.826; Std. = 0.055), the MOMEM is significantly superior to other artificial intelligence-based methods. These analytical results indicate that MOMEM is an innovative tool for accurate calculating soil shear strength; thus, it provides geotechnical engineers with reliable figures to significantly increase soil-related engineering design.

中文翻译：

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基于人工电场算法的高级元学习器优化叠加集成技术提高土壤抗剪强度预测精度

剪切强度是土壤的一个重要特性，被认为是当力作用在土体上时其抵抗破坏的内在能力。这项研究提出了一种先进的元精益法来辨别剪切强度特性并生成对土壤极限剪切强度的可靠估计。所提出的模型被命名为元启发式优化元集成学习模型（MOMEM），旨在帮助岩土工程师准确预测感兴趣的参数。MOMEM 是通过集成人工电场算法 (AEFA) 建立的，以动态混合径向基函数神经网络 (RBFNN) 和多元自适应回归样条 (MARS)。在形成 MOMEM 的框架内，AEFA 通过优化 RBFNN 和 MARS 的控制参数（包括神经元数、高斯扩展、正则化系数和核函数参数）来持续监控 RBFNN 和 MARS 在采矿土壤抗剪强度特性中的学习阶段。同时，RBFNN 和 MARS 通过线性组合方法堆叠，动态权重由 AEFA 元启发式优化。20 次运行的单尾 t 检验证实，RMSE（平均值 = 0.035 kg/cm2；Std. = 0.005 kg/cm2）、MAE（平均值 = 0.026 kg/cm2；Std. = 0.004 kg/cm2)、MAPE（平均值 = 7.9%；Std. = 1.72%）和 R2（平均值 = 0.826；Std. = 0.055），MOMEM 明显优于其他基于人工智能的方法。这些分析结果表明，MOMEM 是一种精确计算土壤抗剪强度的创新工具；因此，它为岩土工程师提供了可靠的数据，以显着增加与土壤相关的工程设计。