A reliable prediction of the soil properties mixed with recycled material is considered as an ultimate goal of many geotechnical laboratory works. In this study, after planning and conducting a series of laboratory works, some basic properties of marine clay treated with recycled tiles together with their unconfined compressive strength (UCS) values were obtained. Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. The best neuro-swarm and neuro-imperialism models were selected based on several parametric studies on the most important and effective parameters of PSO and ICA. Afterward, these models were evaluated according to several well-known performance indices. It was found that the neuro-swarm predictive model provides a higher level of accuracy in predicting the UCS of clay soil samples treated with recycled tiles. However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures.

对混合有回收材料的土壤特性进行可靠预测被认为是许多岩土工程实验室工作的最终目标。在这项研究中，经过一系列实验室工作的规划和实施，获得了用再生瓷砖处理的海洋粘土的一些基本特性及其无侧限抗压强度 (UCS) 值。然后，选择这些基本属性作为输入变量，通过使用两个混合智能系统即神经群和神经帝国主义来预测 UCS 值。实际上，在这些系统中，分别使用粒子群优化 (PSO) 和帝国主义竞争算法 (ICA) 对人工神经网络 (ANN) 的权重和偏差进行了优化，以获得比预先开发的 ANN 模型更高的精度. 基于对 PSO 和 ICA 最重要和最有效参数的多项参数研究，选择了最佳的神经群和神经帝国主义模型。之后，根据几个众所周知的性能指标对这些模型进行了评估。发现神经群预测模型在预测用再生瓷砖处理的粘土土壤样品的 UCS 方面提供了更高水平的准确性。然而，这两种混合预测模型都可以在实践中用于预测岩土结构初始设计的 UCS 值。发现神经群预测模型在预测用再生瓷砖处理的粘土土壤样品的 UCS 方面提供了更高水平的准确性。然而，这两种混合预测模型都可以在实践中用于预测岩土结构初始设计的 UCS 值。发现神经群预测模型在预测用再生瓷砖处理的粘土土壤样品的 UCS 方面提供了更高水平的准确性。然而，这两种混合预测模型都可以在实践中用于预测岩土结构初始设计的 UCS 值。